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Automated leukemia detection in blood microscopic images using statistical texture analysis
Abstract
Pathological image analysis plays a significant role in effective disease diagnostics. Quantitative microscopy has supplemented clinicians with accurate results for diagnosis of dreaded diseases such as leukemia, hepatitis, AIDS, psoriasis. In this paper we present a texture based approach for automated leukemia detection. Acute lymphocytic leukemia (ALL) is a malignant disease characterized by the accumulation of lymphoblast in the bone marrow. Texture features of the blood nucleus are investigated for diagnostic prediction of ALL. Other shape features are also extracted to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast (blasts). Initial segmentation is done using K-means clustering which segregates leukocytes or white blood cells (WBC) from other blood components i.e. erythrocytes and platelets. The results of K-means are used for evaluating individual cell shape, texture and other features for final detection of leukemia. A total of 108 blood smear images were considered for feature extraction and final performance evaluation is validated with the results of a hematologist.
... WBC are responsible for immunity of the human body against infectious diseases. In the case of leukemia, these WBC are immature [1][2][3][4][5][6][7][8]. These immature cells are termed 'lymphoblasts', which prevent healthy red cells, platelets, and mature white cells (leukocytes) from being generated. ...
... It is a relatively fledgling and interdisciplinary technology which combines the primary ideas of Digital Image Processing, computer sciences, blood smear images processing, and artificial intelligence. Multiple efforts have been made to develop a completely automated system for leukemia detection using imageprocessing methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], [33][34][35][36] , [48-66, 73, 80-88]. We present more details about a collection of these systems in Table 1 below (papers [1][2][3][4][5][6][7][8][9][10], [33][34][35][36], [63]). ...
... Multiple efforts have been made to develop a completely automated system for leukemia detection using imageprocessing methods [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], [33][34][35][36] , [48-66, 73, 80-88]. We present more details about a collection of these systems in Table 1 below (papers [1][2][3][4][5][6][7][8][9][10], [33][34][35][36], [63]). These are not for comparison but as a survey of existing representative methodologies. ...
Acute leukaemia is a type of cancer that affects the blood and the bone marrow. Detection and classification of white blood cells is a challenge in image processing, as manual data analysis is time-consuming and most often it is not accurate. Research in this area is essential because a fully automated classifier tool can prove to be an effective ancillary tool for physicians. The goal of this article is to develop a new whole image system that performs automated classification of peripheral blood smear images of acute lymphoblastic leukaemia containing multiple nuclei. This is a key difference of our system from other commonly used systems. For this purpose, we tested the commonly used features in other systems in order to get the most relevant features for our system. Also, we included a new, so-called cell energy, colour feature. In order to evaluate the performance of our system, we used multiple cross-validation methods. Experimental results show that the proposed system is efficient and effective in classification acute leukaemia cells in blood smear images.
... But this way of evaluation reported that, 50% of patients are misdiagnosed in regard of subtypes. Diagnostic problem arises due to imitation of similar signs in other disorders [1] and complex nature of blood smear images. Hence the variation in slide preparation techniques, needs more work to meet real clinical demands. ...
... The acute leukemia segmentation and classification techniques are based on four main categories such as threshold, boundary, region and hybrid. Most of the techniques combines boundary and region criteria [1]- [16]. Threshold based methods such as Otsu and histogram [9] [12] segments the WBCs directly from the blood smear image using the intensity level. ...
... The Nuclei is segmented by curve and corner detection as shown in Fig (4) provides the accurate detection of nuclei and WBCs of the microscopic blood image. Fig 4(a) to 4(e) is the segmentation of WBCs and nuclei from image shown in Fig (3a) shows varying degree of lobulation characterized by delicate folding or increasing of nuclear membrane is processed to segment leukemia from blood images as shown in table (1). It is further classified by Fuzzy C mean clustering as shown in figure (5). ...
Due to complex nature of blood smear images and imitation of similar signs of other disorders makes difficult to detect leukemia. It also needs more time to diagnose and sometimes susceptible to errors. In order to solve this issues fuzzy C means cluster optimization of leukemia detection based on morphological contour segmentation is proposed in this paper. This paper introduces the new approach for leukemia detection which consist of (1) contrast enhancement to highlights the nuclei, (2) morphological contour segmentation, and (3) Fuzzy C means detection of leukemia. The contract enhancement is done by simple addition and subtraction operation to separate the nuclei. The morphological contour segmentation detects the edges of nuclei and eliminate the normal white blood cells from the microscopic blood image. Then the texture, geometry, color and statistical features of nuclei is evaluated to determines the various factors of leukemia. Finally it is trained by Fuzzy C mean clustering of single row feature vector of each cell is used to classify leukemia from white blood cells. This makes the proposed algorithm better results in accuracy and time consumption when compare to normal hematologist's visual classification.
... Gautam and Bhadauria improved the contrast of the blood microscopic image and used Otsu's thresholding for the segmentation of the white cell nucleus [9]. Mohapatra et al. did the preprocessing step by applying the median filter on the images in order to eliminate possible noises and used K-means clustering in the Lab color model to divide pixels of the blood microscopic images [10]. K -means clustering and the Lab color model for segmentation of the white cells nuclei have been also explored [11,12]. ...
... Subsequently, M pilot samples with the lowest con i value will be selected and construct matrix A: (4) In the next step, we will solve the linear equation (10), to calculate linear combination of the M training samples: Computational and Mathematical Methods in Medicine ...
Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.
... Among the unsupervised clustering techniques, the Kmeans [51] and the Fuzzy C-means (FCM) [52] algorithms are the most frequently used for microscopic cell image segmentation. Kmeans clustering on HSV color model or on L * a * b color space was widely proposed to segment microscopic blood images [53][54][55][56][57] . FCM clustering has been successfully applied in the segmentation of these blood images [ 58 , 59 ]. ...
... -Selected markers might not accurately represent cells. Clustering-based approaches K-means and FCM [53][54][55][56][57][58][59] -Simple and easy to implement. ...
Background and objective
Nowadays, the number of pathologies related to food are multiplied. Mycotoxins are one of the most severe food contaminants that cause serious effects on the human health. Therefore, it is necessary to develop an assessment tool for evaluating their impact on the immune response. Recently, a new investigational method using human dendritic cells was endorsed by biologists. Nevertheless, analysis of the morphological features and the behavior of these cells remains merely visual. In addition, this manual analysis is difficult and time-consuming. Here, we focus mainly on automating the evaluation process by using advanced image processing technology.
Methods
An automatic segmentation approach of microscopic dendritic cell images is developed to provide a fast and objective evaluation. First, a combination of K-means clustering and mathematical morphology is used to detect dendritic cells. Second, a region-based Chan-Vese active contour model is used to segment the detected cells more precisely. Finally, dendritic cells are extracted by a filtering based on eccentricity measure.
Results
The proposed scheme is tested on an actual dataset containing 421 microscopic dendritic cell images. The experimental results show high conformity between the results of the proposed scheme and ground-truth elaborated by biological expert. Moreover, a comparative study with other state-of-art segmentation schemes demonstrates the efficiency of the proposed method. It gives the highest average accuracy rate (99.42 %) compared to recent studied approaches.
Conclusions
The proposed image segmentation method for morphological analysis of dendrite inhibition can consistently be used as an assessment tool for biologists to facilitate the evaluation of serious health impacts of mycotoxins.
... A future research perspective had also been presented in [7]. Segmentation and statistical texture analysis based Leukemia detection are shown in [8] and [9], respectively. In [10] and [11], K-means clustering and Fuzzy C means based Leukemia prediction is discussed. ...
... More discussions on the existing approaches of automatic Leukemia predictions are beyond the scope of this paper. The interested reader can see [2,7,9] for supervised and [8,10] for unsupervised approaches. Table I. ...
... • In order to perform reliable diagnosis, the system considers both nucleus and cytoplasm in segmentation and feature extraction. This is different from related state-of-the-art applications in the literature which focused purely on nuclei for performing segmentation of WBCs and arriving at the resulting diagnosis [13][14][15][16] . • The proposed SDM-based clustering takes both within-and between-cluster scatter variances into consideration. ...
... The MLP has the following settings, i.e. two hidden layers, each with 8 and 43 nodes for the first and second evaluation schemes; and one hidden layer with 13 nodes for the third evaluation strategy. As for the SVM, the best parameter settings of (γ, Co) obtained from grid search are (8, 0.5), (8,4), (16,32) and for Dempster-Shafer ensemble, there are 10, 11 and 10 MLP base models employed respectively for the first, second and third schemes. Especially, such ensembles are constructed based on the best trade-off between computational complexity and system performance. ...
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed between-cluster evaluation is formulated based on the trade-off of several between-cluster measures of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty features consisting of shape, texture, and colour information of the nucleus and cytoplasm sub-images are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM) and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation. The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results also compare favourably with those reported in the literature, indicating the usefulness of the proposed SDM-based clustering method.
... • In order to perform reliable diagnosis, the system considers both nucleus and cytoplasm in segmentation and feature extraction. This is different from related state-of-the-art applications in the literature which focused purely on nuclei for performing segmentation of WBCs and arriving at the resulting diagnosis [13][14][15][16] . • The proposed SDM-based clustering takes both within-and between-cluster scatter variances into consideration. ...
... The MLP has the following settings, i.e. two hidden layers, each with 8 and 43 nodes for the first and second evaluation schemes; and one hidden layer with 13 nodes for the third evaluation strategy. As for the SVM, the best parameter settings of (γ, Co) obtained from grid search are (8, 0.5), (8,4), (16,32) and for Dempster-Shafer ensemble, there are 10, 11 and 10 MLP base models employed respectively for the first, second and third schemes. Especially, such ensembles are constructed based on the best trade-off between computational complexity and system performance. ...
This research proposes an intelligent decision support system for acute lymphoblastic leukaemia
diagnosis from microscopic blood images. A novel clustering algorithm with stimulating discriminant
measures (SDM) of both within- and between-cluster scatter variances is proposed to produce robust
segmentation of nucleus and cytoplasm of lymphocytes/lymphoblasts. Specifically, the proposed
between-cluster evaluation is formulated based on the trade-off of several between-cluster measures
of well-known feature extraction methods. The SDM measures are used in conjuction with Genetic
Algorithm for clustering nucleus, cytoplasm, and background regions. Subsequently, a total of eighty
features consisting of shape, texture, and colour information of the nucleus and cytoplasm subimages
are extracted. A number of classifiers (multi-layer perceptron, Support Vector Machine (SVM)
and Dempster-Shafer ensemble) are employed for lymphocyte/lymphoblast classification. Evaluated
with the ALL-IDB2 database, the proposed SDM-based clustering overcomes the shortcomings of
Fuzzy C-means which focuses purely on within-cluster scatter variance. It also outperforms Linear
Discriminant Analysis and Fuzzy Compactness and Separation for nucleus-cytoplasm separation.
The overall system achieves superior recognition rates of 96.72% and 96.67% accuracies using
bootstrapping and 10-fold cross validation with Dempster-Shafer and SVM, respectively. The results
also compare favourably with those reported in the literature, indicating the usefulness of the
proposed SDM-based clustering method.
... • Eccentricity -measure of the deviation of object from being circular. This feature is important as the normal lymphocytes are more circular than blasts [5]. ...
... • Compactness -measure of roundness of the nucleus [5]. ...
Acute Lymphoblastic Leukemia (ALL) is caused due to increase in number of abnormal lymphocyte cells in blood or bone marrow. This paper presents a methodology for automatic detection of the abnormal lymphocytes in a given image of the blood sample. We have used Local Binary Pattern (LBP) features for classifying the lymphocyte cell as blast or normal. LBP texture features of blood nucleus are investigated for the detection of ALL. We have also used shape features for classification and a comparative analysis of both the features is performed. It is seen that the LBP features provide reasonably good accuracy in classification.
... Overall, the U-Net PLR structure is a powerful tool for biomedical photograph segmentation and type obligations. Its encoder-decoder shape, combined with skip connections and PLR activations, permits the community to capture both local and international patterns inside the enter image, main to advanced segmentation and category accuracy [29]. The summary of the proposed work is given in Table 2. Figure 2 illustrates the structure of U-Net with Parametric Leaky ReLU (PLR) activations. ...
In this work, we propose a singular approach for blast identification and classification in Acute Lymphoblastic Leukemia (ALL), an ordinary kind of formative child cancer dataset. The proposed method combines the Pivot-Growing Segmentation (PGS) algorithm with the U-Net structure better with Parametric Leaky ReLU (PLR) activations. The Pivot-Growing Segmentation has set of rules to clustering method that utilizes K-medoid and squared Euclidean distance as a similarity degree. In this context, it's far used to delineate blast areas from microscopic images by imparting unique localization. This technique is hired to improve the accuracy of blast identification, that's vital for accurate diagnosis and treatment of cancer. The U-Net PLR version is then used for blast classification that is a fully linked Convolutional Neural Network (CNN) with Parametric Leaky ReLU activations. This version is designed to extract difficult capabilities from segmented areas, improving the type overall performance. The U-Net PLR version includes an encoder and decoder structure, with bypass connections among the corresponding layers. The encoder is accountable for extracting capabilities from the input image, while the decoder reconstructs the image and outputs the segmentation mask. The proposed method is achieving overall performance in blast identification and classification of the given dataset. The proposed technique offers a promising path for boosting diagnostic accuracy and assisting in personalized treatment techniques for pediatric sufferers with ALL.
... For feature extraction like shape and texture, gray level co-occurance has been used. In [27] authors have proposed a shape-based feature extraction method to detect the different shape of leukemia cells. This method detects different shapes like circle, rectangle, ellipse etc. Otsu's thresholding is used to convert images into binary and then morphological operation including dilation, area opening, erosion is used to remove false pixels. ...
Leukaemia is a kind of cancer that damages the white blood cells and disturb the
bone marrow of human body. It produces cancerous blood cells that disturb the
human's immune system and have signi�cant e�ects over the production ability of
bone marrow to e�ectively create di�erent types of blood cells like red blood cells
(RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed
manually by taking a complete blood count test of the patient's blood from which
medical professionals can investigate the signs of leukemia cell. Furthermore, two
other methods consisting of a microscopic inspection of blood smears and bone
marrow aspiration are also utilized while examining the patient for leukemia. But
all these methods are labor-intensive, slow, inaccurate and required a lot of human
experience and dedication. To overcome these limitations, di�erent authors
have proposed automated detection systems for leukemia diagnosing. They have
deployed digital image processing and machine learning algorithms to classify the
cells into normal and blast cells. Although these systems are more e�cient, reliable
and fast as compare to previous manual diagnosing methods. But due to
complex characteristics of blood images and leukemia cells having much intraclass
variability as well as inter-class similarity more work needed to be done for the
classi�cation of leukemia cells. In this thesis, we have proposed a robust automated
system to diagnose leukemia and its subtypes. We have classi�ed ALL into
its subtypes based on FAB classi�cation i.e. L1, L2 and L3 types with better performance.
We have achieved better accuracy as compared to the state-of-the-art
methodologies.
... The cluster of differentiation (CD) marker with morphological features is preferred by medical experts for the classification of leukemia cells (Laosai and Chamnongthai 2018;Mohapatra, Patra, and Satpathy 2011). Various geometrical and moment-based features are also analyzed to assimilate the variations in healthy and blast cells. ...
Acute lymphoblastic leukemia (ALL) in human white blood cells is hazardous and requires immediate clinical interventions. The main objective of the proposed work is to suggest the predominant features for detection of ALL. The input images are obtained from public database ‘ALL-IDB2ʹ. All the obtained images are resized into a uniform size. The nucleus of both healthy and blast cells is segmented using UNET. Thousand deep features are extracted from the fully connected layer of different convolutional neural network models such as AlexNet, GoogleNet and SqueezeNet, and all features are fused together. The distinct features are selected using mutual information (MI), minimum recursive maximal relevance (mRmR) and recursive feature elimination (RFE) based methods. Furthermore, the intersection of selected features is carried out to obtain the prominent deep features, which are examined by heatmap. Finally, the statistical analysis is carried out with consistent and robust feature sets using ANOVA. It is found that 50% of the fused deep features seem to be better with p = . 00001. The performance of the proposed system without feature fusion is also observed. It is detected that fused features are more suitable to discriminate the healthy and blast cells to identify ALL and support clinical decisions.
... Some methods extract the features of the cytoplasm and nucleus of WBCs and then used the neural network classifier to distinguish them. To classify the WBCs, Mohapatra et al. [29] used some shape features of leukocytes and a grey level co-variance matrix. In contrast, Sinha and Ramakrishnan [27] used texture feature, color, and shape features of the leukocytes. ...
Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed.
Methods: The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm.
Results: The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out.
Conclusions: The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.
... To select the colour tones in an image and apply a filter on the particular region, HSV colour space is used. This helps in eliminating dust, scratches and noise [12]. ...
... On the other hand, authors in [15] proposes a functional Link Neural Architecture for ALL detection. Statistical Texture Analysis was done to detect ALL in [16]. In [19], a high throughput screening algorithm for leukemia cells is proposed. ...
In this era, surrounded by numerous technologies,
medical sector has seen a lot of advancement through
implementing various autonomous systems to identify different
types of diseases. In this paper, a framework for identification
of Acute Lymphoblastic Leukemia from the microscopic image
of white blood cell is proposed. Microscopic images are at first
carefully preprocessed to prepare them for classification. In
addition, four different machine learning algorithms, namely,
Random Forest (RF), Support Vector Machine (SVM), Logistic
Regression (LR), and Decision Tree (DT) are applied and
respective results are analyzed to provide a comparison between
these algorithms in terms of different performance metrics.
After a thorough comparison, it is observed that the SVM
works well to classify and identify the Acute Lymphoblastic
cell which is responsible for Leukemia cancer.
... The utilization of image processing technique as an automated counting system for diagnosis of leukemia disease has been studied in [5] and [6] in which the image segmentation technique such as K-mean clustering is employed in several leukemia cases. In [7], K-mean clustering is used for automated acute lymphocytic leukemia detection. While in [8], K-mean clustering is integrated with histogram equalization and Zack algorithm to determine leukemia. ...
... where k is the number of clusters, i represents number of iteration over the intensities, j represents the number of iteration over all the centroids and µ i are the mean of intensities (Sinha and Ramakrishnan, 2003) used k-means algorithm for nucleus region detection of white cells (Rajendransup et al., 2011) employed k-means for segmentation of white blood cells. The k-means clustering is used for discriminating nucleus from the background into L*a*b colour space (Mohapatra et al., 2011). The result of the segmentation of the nucleus is shown in Figure 5. ...
... In a conventional setup, the pathologist plays a crucial role in accurate diagnosis of ALL; since manual detection process is tedious, time consuming and accuracy of diagnosis also depends upon the experience of pathologist. Recent advances in digital Image processing technology has led to a lot of research towards the development of automated recognition systems for identification of ALL [5][6][7]. CAD systems have the potential to provide valuable assistance to the pathologist in determination of the presence or the absence of the disease. In addition, it may also help in evaluation of stage of progression of disease. ...
Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.
... This algorithm is used to divide the image into clusters by using observations so that the objects in the same cluster are as close as possible and objects in the different cluster are different from other cluster's objects. This method has been used in the leukaemia blood image segmentation to extract the WBCs and lymphocytes from the image [35,36]. ...
Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.
... In a conventional setup, the pathologist plays a crucial role in accurate diagnosis of ALL; since manual detection process is tedious, time consuming and accuracy of diagnosis also depends upon the experience of pathologist. Recent advances in digital Image processing technology has led to a lot of research towards the development of automated recognition systems for identification of ALL [5][6][7]. CAD systems have the potential to provide valuable assistance to the pathologist in determination of the presence or the absence of the disease. In addition, it may also help in evaluation of stage of progression of disease. ...
... Many papers extract some features of nucleus and cytoplasm, and then distinguish them with some neural networks classifiers. For example, Mohapatra et al. [28] use gray level co-occurrence matrix (GLCM) as well as some shape features of the leukocytes to classify five types. Sinha and Ramakrishnan [29] use shape, color and texture features to identify leukocyte. ...
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R, B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.
... Statistical moments of intensity histogram of an image is one of the simplest methods to extract texture-related information (Gonzalez and Woods 2002), but it carries only information about the distribution of intensities not the relative positions of pixels. Co-occurrence matrix as a statistical image analysis approach can help to provide valuable texture information by considering the relative position of the neighboring pixels in an image (Conners and Harlow 1980;Haralick et al. 1973) and have been found useful in a number of image processing tasks (Joseph and Balakrishnan 2011;Mohapatra et al. 2011;Qurat-Ul-Ain et al. 2010). Here, we introduced the use of concurrence matrix and Haralick features which are highly dependent on the texture information of an image to extract information from cellular automata image of proteins and determine the structural class of protein sequence. ...
Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.
... In a conventional setup, the pathologist plays a crucial role in accurate diagnosis of ALL; since manual detection process is tedious, time consuming and accuracy of diagnosis also depends upon the experience of pathologist. Recent advances in digital Image processing technology has led to a lot of research towards the development of automated recognition systems for identification of ALL [5][6][7]. CAD systems have the potential to provide valuable assistance to the pathologist in determination of the presence or the absence of the disease. In addition, it may also help in evaluation of stage of progression of disease. ...
... They first separate WBCs from other blood cells, then morphological indexes are extracted and a neural classifier is used for classification. Automated leukemia detection in blood microscopic images is performed in [15] using statistical texture analysis. Texture and shape features are used to classify a lymphocytic cell in the blood image into normal lymphocyte or lymphoblast. ...
Leukemia (a cancer of leukocytes) basically develops in the bone marrow. Acute myelogenous leukemia (a type of leukemia) has eight sub-types according to French-American-British classification. These forms can be visually observed by pathologists using microscopic images of infected cells. However, identification task is tedious and usually difficult due to varying features. Automatic leukemia detection is an important topic in the domain of cancer diagnosis. This paper presents a novel method based on dictionary learning and sparse representation for detecting and classification of different sub-types of AML. For each class, two intensity and label dictionaries are designed for representation using image patches of training samples. New image is represented by all dictionaries and the one with minimum error determine the type of class. We considered M2, M3 and M5 sub-types for evaluation of the method. The initial implementing of the proposed method achieved 97.53% average accuracy for different sub-types of AML.
... It continues assigning and recomputing until convergence is attained or no pixel changes its cluster. Previously, K-means was used for nucleus and cytoplasm detection using an expectation maximization algorithm (Sinha & Ramakrishnan, 2003), segmenting nucleus, cytoplasm, red blood cells and background (Mohapatra et al., 2011), and leukocyte segmentation (Ko et al., 2011). Fuzzy c-means uses partial membership of any data sample to all clusters in comparison with K-means. ...
Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively.
... ALL is diagnosed through microscopic inspection of blood or bone marrow samples by the pathologists. This manual method of diagnosis is tedious and time consuming [12]. In the automatic detection of ALL, image processing techniques are employed for identifying blast cells. ...
Acute Lymphoblastic Leukemia (ALL) is a type of cancer characterized by increase in abnormal white blood cells in the blood or bone marrow. This paper presents a methodology to detect ALL automatically using shape features of the lymphocyte cell extracted from its image. We apply Correlation based Feature Selection technique to find a prominent set of features which can be used to predict a lymphocyte cell as normal or blast. The experiments are performed on 260 blood microscopic images of lymphocyte and an accuracy of 92.30% is obtained with a set of sixteen features.
... To identify different tissues or cellular components, histological sections are segmented according to colour, shape or texture features after acquired with high-resolution digital camera, and then classified by commonly employing supervised methods [3][4][5][6]. Several methods based on digital image processing and pattern recognition techniques have been proposed to deal with histological image segmentation problem in the past years [7][8][9][10][11][12]. When no training data set is available in histological image analysis, feature vectors or points representing pixels in the histological image are usually extracted from their local properties. ...
Colour image segmentation is fundamental and critical for quantitative histological image analysis. The complexity of the microstructure and the approach to make histological images results in variable staining and illumination variations. And ultra-high resolution of histological images makes it is hard for image segmentation methods to achieve high-quality segmentation results and low computation cost at the same time.
Mean Shift clustering approach is employed for histological image segmentation. Colour histological image is transformed from RGB to CIE L*a*b* colour space, and then a* and b* components are extracted as features. To speed up Mean Shift algorithm, the probability density distribution is estimated in feature space in advance and then the Mean Shift scheme is used to separate the feature space into different regions by finding the density peaks quickly. And an integral scheme is employed to reduce the computation cost of mean shift vector significantly. Finally image pixels are classified into clusters according to which region their features fall into in feature space.
Numerical experiments are carried on liver fibrosis histological images. Experimental results demonstrate that Mean Shift clustering achieves more accurate results than k-means but is computational expensive, and the speed of the improved Mean Shift method is comparable to that of k-means while the accuracy of segmentation results is the same as that achieved using standard Mean Shift method.
An effective and reliable histological image segmentation approach is proposed in this paper. It employs improved Mean Shift clustering, which is speed up by using probability density distribution estimation and the integral scheme.
... Also, blood images obtained by microscopes can be more easily transmitted to clinical centers than liquid blood samples. So there is always a need for a cost effective and robust automated system for leukemia screening which can greatly improve the output without being influenced by operator fatigue [3]. Several attempts of partial/full automated systems for leukemia detection based on image-processing systems are present in literature but they are still at prototype stage [12][13][14]. ...
CITATION: M. Madhukar, S. Agaian, A.T. Chronopoulos, New decision support tool for acute lymphoblastic leukemia classification, Proc. SPIE 8295, Image Processing: Algorithms and Systems X; and Parallel Processing for Imaging Applications II, IS and T SPIE, San Francisco, Vol. 8295, pp. 829518-1-829518-12, 22–26 January 2012.
SUMMARY:
In this paper, we build up a new decision support tool to improve
treatment intensity choice in childhood ALL. The developed system
includes different methods to accurately measure furthermore cell
properties in microscope blood film images. The blood images are exposed
to series of pre-processing steps which include color correlation, and
contrast enhancement. By performing K-means clustering on the resultant
images, the nuclei of the cells under consideration are obtained. Shape
features and texture features are then extracted for classification. The
system is further tested on the classification of spectra measured from
the cell nuclei in blood samples in order to distinguish normal cells
from those affected by Acute Lymphoblastic Leukemia. The results show
that the proposed system robustly segments and classifies acute
lymphoblastic leukemia based on complete microscopic blood images.
Рассматриваемая работа относится к направлению автоматизации медицинской диагностики с применением компьютерной микроскопии. Исследуется влияние фокусировки микроскопа на текстурные характеристики изображений в системе компьютерной микроскопии при решении диагностических задач в онкоморфологии для распознавания злокачественных опухолей. Особую важность указанные вопросы имеют при решении задачи анализа изображений слабоконтрастных объектов - хроматина ядра клеток костного мозга в процессе диагностики опасных онкологических заболеваний системы крови – острых лейкозов. В ходе проведенного эксперимента в качестве исследуемых образцов использовались препараты костного мозга пациентов с острым лимфобластным лейкозом. Препараты предоставлены Лабораторией иммунологии гемопоэза Национального медицинского исследовательского центра онкологии им. Н.Н. Блохина. По результатам эксперимента среди рассмотренных характеристик изображений структуры хроматина ядер клеток костного мозга выявлена высокая чувствительность к фокусировке оптической системы микроскопа текстурной характеристики «момент инерции» красной компоненты R цветовой модели RGB. Приведены практические рекомендации для разработчиков автоматизированных систем по использованию аппарата текстурного анализа в процессе проектирования систем диагностики онкологических заболеваний, основанных на микроскопических методах исследования образцов биологических материалов.
This work relates to the direction of automation of medical diagnostics using computer microscopy. The effect of focusing a microscope on the textural characteristics of chromatin images of the nuclei of bone marrow cells in the computer microscopy system when solving diagnostic problems in oncomorphology for the recognition of malignant tumors is investigated. These questions are of particular importance when solving the problem of analyzing images of low-contrast objects-chromatin of the nucleus of bone marrow cells in the diagnosis of dangerous oncological diseases of the blood system-acute leukemia. During the experiment, bone marrow preparations from patients with acute lymphoblastic leukemia were used as test samples. The preparations were provided by the laboratory of hematopoiesis immunology of the N.N. Blokhin National Medical Research Center of Oncology. The results of the experiment among the characteristics of images of the structure of the chromatin of the nuclei of bone marrow cells revealed the high sensitivity of the focusing optical system of the microscope texture characteristic «moment of inertia» of the red components R of RGB color model. Practical recommendations are given for developers of automated systems on the use of the texture analysis apparatus in the design of cancer diagnostics systems based on microscopic methods of studying samples of biological materials.
Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for White Blood Cell (WBC) Counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an Intra-nucleus Mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from non-leukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n=260) is composed of healthy and Acute Lymphoid Leukemia (ALL) single cell images, and second database-D2 contains Acute Myeloid Leukemia (AML) blasts (n=3,294) and non-blast (n=15,071) cell images. In a first experiment, blasts vs non-blast differentiation is performed by training with a subset of D2 (n=6,588) and testing in D1 (n=260), obtaining a training AUC of 0.991±0.002 and AUC=0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from non-blast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score. This article is protected by copyright. All rights reserved.
Leukemia is a type of blood cancer that normally originates in the bone marrow. It causes a relatively large number of abnormal blood cells to be produced. In a normal, healthy state, blood cells originate in the bone marrow as stem cells and later mature to form different types of blood cells (red blood cells, white blood cells, or platelets) and transfer to the bloodstream. As for a person suffering from leukemia, his/her bone marrow begins to produce abnormal white blood cells that enter the bloodstream and begin to compete against the normal healthy blood cells and prevent them from performing their functions properly. This paper aims to detect blood cancer disease (Leukemia) at the earliest stage possible through the use of image processing techniques, k-means clustering algorithm, and support vector machine (SVM). The process will be further supported by processing tools such as MATLAB to ensure the early identification of any leukemia existence, thus enabling early administration of appropriate treatment to mitigate possible pestilent outcomes. The detection through using the images is a cheap, fast, and safe method as there no need for specialized equipment used for lab testing.
Leukemia and lymphoma are the neoplastic proliferative disorders of white blood cells (WBCs), which increases the malignant lymphoblast cells in the blood. The objective of this work is to perform segmentation of white blood cell image into dissimilar region and then analyze those reasons for particular application in hematology to localize the lymphoblast cells. The population of lymphoblast cells is enormously grows in bone marrow. Acute leukemia often causes erroneous diagnosis due to its imprecise nature. Moreover, due to the rapid growth of the disease, it may damage the normal blood cells and cause death. Early prediction and diagnosis are very significant and conscientious factor for the hematologist. In this work, an unsupervised learning algorithm, namely k-means is used for segmenting the nucleus and cytoplasm. Various color and geometric features are extracted to analyze the malignant and healthy leukocyte cells. These features are then subjected to the feature elimination to reduce the high dimensional feature space into optimal sized features set using principal component analysis. Furthermore, various binary classifiers like SVM, PNN, k-NN, and SSVM are used for classifying the extracted regions into nucleus, cytoplasm, and background cells. The results yielded through the experimentations establish that the proposed system successfully eradicate the extraneous features, effectively classify the malignant lymphoblast cells from the healthy lymphocytes and preserves the maximum accuracy in comparison to that of other methods.KeywordsWhite blood cells (WBCs)Hematological analysisLymphoblast cell localizationAcute leukemiaGeometric featuresColor featuresk-means clustering
The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from non-cancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised of 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and DenseNet201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved the best performance in diagnosis and classification. Finally, a model was developed and proposed based on this state-of-the-art technology. This deep learning-based model attained an accuracy, sensitivity, and specificity of 99.85, 99.52, and 99.89%, respectively. The proposed method may help to distinguish ALL from benign cases. This model is also able to assist hematologists and laboratory personnel in diagnosing ALL subtypes and thus determining the treatment protocol associated with these subtypes. The proposed data set is available at https://www.kaggle.com/mehradaria/leukemia and the implementation (source code) of the proposed method is made publicly available at https://github.com/MehradAria/ALL-Subtype-Classification.
Computer Aided Diagnosis (CAD) systems can support physicians in classifying different kinds of breast cancer, liver cancer and blood tumours also revealed by images acquired via Computer Tomography, Magnetic Resonance, and Blood Smear systems. In this regard, this survey focuses on papers dealing with the description of existing CAD frameworks for the classification of the three mentioned diseases, by detailing existing CAD workflows based on the same steps for supporting the diagnosis of these tumours. In detail, after an appropriate acquisition of the images, the fundamental steps carried out by a CAD framework can be identified as image segmentation, feature extraction and classification. In particular, in this work, specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods. The cited methodology is focused on sharing and reviewing an amount of specific works. Then, the performance of three selected case studies are carefully reported, designed with the aim of showing how final outcomes can vary according to different choices in each step of the adopted workflow. More in detail, these case studies concern with breast images acquired by Tomosynthesis and Magnetic Resonance, hepatocellular carcinoma images acquired by Computed Tomography and enhanced by a triphasic protocol with a contrast medium, peripheral blood smear images for cellular blood tumours and are used to compare their performance.
This paper proposes an automatic and robust decision support system for accurate acute leukemia diagnosis from blood microscopic images. It is a challenging issue to segment leukocytes under uneven imaging conditions since features of microscopic leukocyte images change in different laboratories. Therefore, this paper introduces an automatic robust method to segment leukocyte from blood microscopic images. The proposed robust segmentation technique was designed based on the fact that if background and erythrocytes could be removed from the blood microscopic image, the remainder area will indicate leukocyte candidate regions. A new set of features based on hematologist visual criteria for the recognition of malignant leukocytes in blood samples comprising shape, color, and LBP-based texture features are extracted. Two new ensemble classifiers are proposed for healthy and malignant leukocytes classification which each of them is highly effective in different levels of analysis. Experimental results demonstrate that the proposed approach effectively segments leukocytes from various types of blood microscopic images. The proposed method performs better than other available methods in terms of robustness and accuracy. The final accuracy rate achieved by the proposed method is 98.10% in cell level. To the best of our knowledge, the image level test for acute lymphoblastic leukemia (ALL) recognition was performed on the proposed system for the first time that achieves the best accuracy rate of 89.81%.
Leukemia is a fast growing cancer also called as blood cancer. It normally originates near bone marrow. The need for automatic leukemia detection system rises ever since the existing working methods include labor-intensive inspection of the blood marking as the initial step in the direction of diagnosis. This is very time consuming and also the correctness of the technique rest on the workertextquoterights capability. This paper describes few image segmentation and feature extraction methods used for leukemia detection. Analyzing through images is very important as from images; diseases can be detected and diagnosed at earlier stage. From there, further actions like controlling, monitoring and prevention of diseases can be done. Images are used as they are cheap and do not require expensive testing and lab equipment. The system will focus on white blood cells disease, leukemia. Changes in features will be used as a classifier input.
In present scenario, hematological disorders of leukocyte (WBC) are very frequent in medical practices. This work proposes a novel technique to differentiate ALL (acute lymphoblastic leukemia) lymphoblast cells from healthy lymphocytes. The technique first separate leukocytes from the other blood cells and then lymphocytes are extracted. In this context, a novel computer aided diagnostic system (CAD) is designed for detection of hematological disorders like leukemia (blood cancer) based on Gray level co–occurrence matrices (GLCM) and shape based features. The features thus extracted classified by the auto support vector machine (SVM) binary classifier to find the presence of lymphoblast cell (leukemic cells). GLCM texture feature with feature vector length 13 reveals, classification accuracies of 86.7% and 72.4% for cytoplasm and nucleus respectively while for shape based features illustrated, classification accuracies of 56.1% and 72.4% respectively for a feature vector length 11 in both regions of lymphocyte. The classification accuracy of combined texture-shape feature is 89.8% with feature vector length 37 which shows better results as compared to an individual.
In recent years, Detection of Acute Myelogenous Leukemia (AML) is still a time consuming process. Microscopic tests of stained blood smear and bone marrow is the first step towards diagnosis. In order to detect the AML easily, segmentation with number of features should be highly concentrated. In my proposed system, we are combining three features. They are color features, shape features and texture features. These features are classified and extracted by Local Binary Pattern (LBP). Selection of best features can be given to the Support Vector Machine (SVM) classifier and it will find the cancer cells instantly. Simulations are carried out using Matlab.
In recent years, Detection of Acute Myelogenous Leukemia (AML) is still a time consuming process. Microscopic tests of stained blood smear and bone marrow is the first step towards diagnosis. In order to detect the AML easily, segmentation with number of features should be highly concentrated. In my proposed system, we are combining three features. They are color features, shape features and texture features. These features are classified and extracted by Local Binary Pattern (LBP). Selection of best features can be given to the Support Vector Machine (SVM) classifier and it will find the cancer cells instantly. Simulations are carried out using Matlab.
In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.
CITATION: S. Agaian, M. Madhukar, A. T. Chronopoulos, Automated Screening System for Acute Myelogenous Leukemia Detection in Blood Microscopic Images, IEEE Systems Journal, vol. 8, no. 3, pp. 995–1004, March 2014.
SUMMARY: Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults. The average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming, and its accuracy depends on the operator's ability. In this paper, a simple technique that automatically detects and segments AML in blood smears is presented. The proposed method differs from others in: 1) the simplicity of the developed approach; 2) classification of complete blood smear images as opposed to subimages; and 3) use of these algorithms to segment and detect nucleated cells. Computer simulation involved the following tests: comparing the impact of Hausdorff dimension on the system before and after the influence of local binary pattern, comparing the performance of the proposed algorithms on subimages and whole images, and comparing the results of some of the existing systems with the proposed system. Eighty microscopic blood images were tested, and the proposed framework managed to obtain 98% accuracy for the localization of the lymphoblast cells and to separate it from the subimages and complete images.
Automatic quantification and classification of leukocytes in microscopic images are of paramount importance in the perspective of disease identification, its progress and drugs development. Extracting numerical values of leukocytes from microscopic images of blood or tissue sections represents a tricky challenge. Research efforts in quantification of these cells include normalization of images, segmentation of its nuclei and cytoplasm followed by their classification. However, there are several related problems viz., coarse background, overlapped nuclei, conversion of 3-D nuclei into 2-D nuclei etc. In this review, we have categorized, evaluated, and discussed recently developed methods for leukocyte identification. After reviewing these methods and finding their constraints, a future research perspective has been presented. Further, the challenges faced by the pathologists with respect to these problems are also discussed.
Texture is a very important image feature extremely used in
various image processing problems. It has been shown that humans use
some perceptual textural features to distinguish between textured images
or regions. Some of the most important features are coarseness,
contrast, direction and busyness. In this paper a new method based on
the autocovariance function to estimate quantitatively these features is
shown and the correspondence between these computational measures and
the psychological ones made by human subjects is shown using some
psychometric method
A technique for automating the differential count of blood is presented. The proposed system takes, as input, color images of stained peripheral blood smears and identifies the class of each of the white blood cells (WBC), in order to determine the count of cells in each class. The process involves segmentation, feature extraction and classification. WBC segmentation is a two-step process carried out on the HSV-equivalent of the image, using k-means clustering followed by the EM-algorithm. Features extracted from the segmented cytoplasm and nucleus, are motivated by the visual cues of shape, color and texture. Various classifiers have been explored on different combinations of feature sets. The results presented are based on trials conducted with normal cells. For training the classifiers, a library set of 50 patterns, with about 10 samples from each class, is used. The test data, disjoint from the training set, consists of 34 patterns, fairly represented by every class. The best classification accuracy of 97% is obtained using neural networks, followed by 94% using SVM.
While the early diagnosis of hematopoietic system disorders is very important in hematology, it is a highly complex and time consuming task. The early diagnosis requires a lot of patients to be followed-up by experts which, in general is unfeasible because of the required number of experts. The differential blood counter (DBC) system that we have developed is an attempt to automate the task performed manually by experts in routine. In our system, the cells are segmented using active contour models (snakes and balloons), which are initialized using morphological operators. Shape based and texture based features are utilized for the classification task. Different classifiers such as k-nearest neighbors, learning vector quantization, multi-layer perceptron and support vector machine are employed.
In this survey we review the image processing literature on the various approaches and models investigators have used for texture. These include statistical approaches of autocorrelation functions, optical transforms, digital transforms, textural edgeness, structural element, gray tone co-occurrence, run lengths, and autoregressive models. We discuss and generalize some structural approaches to texture based on more complex primitives than gray tone. We conclude with some structural-statistical generalizations which apply the statistical techniques to the structural primitives. -Author
Segmentation is one of the essential problems in image processing. The objective of this paper is to segment human skin area in the given color image. In this model, skin detection using cluster based technique is built to detect skin areas. The proposed model has been tested on various images and achieved high detection rate.
The problem of image texture analysis is introduced, and existing approaches are surveyed. An empirical evaluation method is applied to two texture measurement systems, co-occurrence statistics and augmented correlation statistics. A spatial-statistical class of texture measures is then defined and evaluated. It leads to a simple class of texture energy transforms, which perform better than any of the preceding methods. These transforms are very fast, and can be made invariant to changes in luminance, contrast, and rotation without histogram equalization or other preprocessing. Texture energy is measured by filtering with small masks, typically 5x5, then with a moving-window average of the absolute image values. This method, similar to human visual processing, is appropriate for textures with short coherence length or correlation distance. The filter masks are integer-valued and separable, and can be implemented with one-dimensional or 3x3 convolutions. The averaging operation is also very fast, with computing time independent of window size. Texture energy planes may be linearly combined to form a smaller number of discriminant planes. These principal component planes seem to represent natural texture dimensions, and to be more reliable texture measures than the texture energy planes. Texture segmentation or classification may be accomplished using either texture energy or principal component planes as input. This study classified 15x15 blocks of eight natural textures. Accuracies of 72% were achieved with co-occurrence statistics, 65% with augmented correlation statistics, and 94% with texture energy statistics. (Author)
List of Algorithms. Preface. Possible Course Outlines. 1. Introduction. 2. The Image, Its Representations and Properties. 3. The Image, Its Mathematical and Physical Background. 4. Data Structures for Image Analysis. 5. Image Pre-Processing. 6. Segmentation I. 7. Segmentation II. 8. Shape Representation and Description. 9. Object Recognition. 10. Image Understanding. 11. 3d Geometry, Correspondence, 3d from Intensities. 12. Reconstruction from 3d. 13. Mathematical Morphology. 14. Image Data Compression. 15. Texture. 16. Motion Analysis. Index.
Eighty-four children with acute lymphoblastic leukaemia (ALL) who had relapsed in bone marrow were studied to assess whether treatment would be more successful if relapse was detected before the disease became clinically evident. Patients whose relapse was detected by routine bone marrow examination before the disease became apparent were compared with those whose relapse was suspected from clinical examination or peripheral blood findings. In the former there was a lower percentage of blast cells in the marrow (p less than 0.02) and the patients suffered less from complications of the disease, but there was no difference in the incidence or duration of second remissions between the two groups.
A part of our research work on an Automated Cell Count project is described. A major requirement for this project is an efficient method to segment cell images. This work presents an accurate segmentation method for an automatic count of white blood cells. First a simple thresholding approach is applied to give initial labels to pixels in the blood cell images. The algorithm is based on priori information about blood smear images. Then the labels are adjusted with a shape detection method based on large regional context information to produce meaningful results. This approach makes use of knowledge of the blood cell structure. The experimental result shows that this method is more powerful than traditional methods that use only local context information. It can perform accurate segmentation of white blood cells even if they have unsharp boundaries.
Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
Deveopmant of impulse noise detection schemes for selective filtering. Master's thesis, National Institute of Technolgy Rourkela
- S Mohapatra
S. Mohapatra. Deveopmant of impulse noise detection schemes for selective filtering. Master's thesis, National Institute of Technolgy Rourkela, 2008.
An automated differential blood count system
- G Ongun
- U Halici
- K Leblebiicioglu
- V Atalay
- M Beksac
- S Beksak
An accurate segmentation method for white blood cell images
- G Ongun
- U Halici
- K Leblebiicioglu
- V Atalay
- M Beksac
- S Beksak