[Show abstract][Hide abstract] ABSTRACT: In the diagnosis of preinvasive breast cancer, some of the intraductal proliferations pose a special challenge. The continuum of intraductal breast lesions includes the usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), and ductal carcinoma in situ (DCIS). The current standard of care is to perform percutaneous needle biopsies for diagnosis of palpable and image-detected breast abnormalities. UDH is considered benign and patients diagnosed UDH undergo routine follow-up, whereas ADH and DCIS are considered actionable and patients diagnosed with these two subtypes get additional surgical procedures. About 250 000 new cases of intraductal breast lesions are diagnosed every year. A conservative estimate would suggest that at least 50% of these patients are needlessly undergoing unnecessary surgeries. Thus, improvement in the diagnostic reproducibility and accuracy is critically important for effective clinical management of these patients. In this study, a prototype system for automatically classifying breast microscopic tissues to distinguish between UDH and actionable subtypes (ADH and DCIS) is introduced. This system automatically evaluates digitized slides of tissues for certain cytological criteria and classifies the tissues based on the quantitative features derived from the images. The system is trained using a total of 327 regions of interest (ROIs) collected across 62 patient cases and tested with a sequestered set of 149 ROIs collected across 33 patient cases. An overall accuracy of 87.9% is achieved on the entire test data. The test accuracy of 84.6% is obtained with borderline cases (26 of the 33 test cases) only, when compared against the diagnostic accuracies of nine pathologists on the same set (81.2% average), indicates that the system is highly competitive with the expert pathologists as a stand-alone diagnostic tool and has a great potential in improving diagnostic accuracy and reproducibility when used as a “second reader-
1D; in conjunction with the pathologists.
Full-text · Article · Aug 2011 · IEEE Transactions on Biomedical Engineering
[Show abstract][Hide abstract] ABSTRACT: Skeletal muscles consist of muscle fibers that are responsible for contracting and generating force. Skeletal muscle fibers are categorized into distinct subtypes based on several characteristics such as contraction time, force production and resistance to fatigue. The composition of distinct muscle fibers in terms of their number and cross-sectional areas is characterized by a histological examination. However, manual delineation of individual muscle fibers from digitized muscle histology tissue sections is extremely time-consuming. In this study, we propose an automated image analysis system for quantitative characterization of muscle fiber type composition. The proposed system operates on digitized histological muscle tissue slides and consists of the following steps: segmentation of muscle fibers, registration of successive slides with distinct stains, and classification of muscle fibers into distinct subtypes. The performance of the proposed approach was tested on a dataset consisting of 25 image pairs of successive muscle histological cross-sections with different ATPase stain. Experimental results demonstrate a promising overall segmentation and classification accuracy of 89.1% in identifying muscle fibers of distinct subtypes.
No preview · Article · Feb 2011 · Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society
[Show abstract][Hide abstract] ABSTRACT: Follicular lymphoma (FL) is one of the most common lymphoid malignancies in the western world. FL has a variable clinical course, and important clinical treatment decisions for FL patients are based on histological grading, which is done by manually counting the large malignant cells called centroblasts (CB) in ten standard microscopic high-power fields from H&E-stained tissue sections. This method is tedious and subjective; as a result, suffers from considerable inter and intrareader variability even when used by expert pathologists. In this paper, we present a computer-aided detection system for automated identification of CB cells from H&E-stained FL tissue samples. The proposed system uses a unitone conversion to obtain a single-channel image that has the highest contrast. From the resulting image, which has a bimodal distribution due to the H&E stain, a cell-likelihood image is generated. Finally, a two-step CB detection procedure is applied. In the first step, we identify evident nonCB cells based on size and shape. In the second step, the CB detection is further refined by learning and utilizing the texture distribution of nonCB cells. We evaluated the proposed approach on 100 region-of-interest images extracted from ten distinct tissue samples and obtained a promising 80.7% detection accuracy.
Preview · Article · Oct 2010 · IEEE transactions on bio-medical engineering
[Show abstract][Hide abstract] ABSTRACT: Pathology slides are diagnosed based on the histological descriptors extracted from regions of interest (ROIs) identified on each slide by the pathologists. A slide usually contains multiple regions of interest and a positive (cancer) diagnosis is confirmed when at least one of the ROIs in the slide is identified as positive. For a negative diagnosis the pathologist has to rule out cancer for each and every ROI available. Our research is motivated toward computer-assisted classification of digitized slides. The objective in this study is to develop a classifier to optimize classification accuracy at the slide level. Traditional supervised training techniques which are trained to optimize classifier performance at the ROI level yield suboptimal performance in this problem. We propose a multiple instance learning approach based on the implementation of the large margin principle with different loss functions defined for positive and negative samples. We consider the classification of intraductal breast lesions as a case study, and perform experimental studies comparing our approach against the state-of-the-art.
[Show abstract][Hide abstract] ABSTRACT: One way of evaluating muscle quality is to determine its fiber type composition in histological sections. A complete muscle fiber type characterization system requires combining information from successive muscle histology images with different ATPase stain. Due to the local and global deformations introduced in slide preparation process, a precise non-rigid registration is essential to construct the spatial correspondences between these successive images. This study proposes an approach for automated non-rigid registration of successive muscle histological sections. We propose a feature-based registration that uses a two stage approach: a rigid initialization followed by a non-rigid refinement. The rigid initialization step globally aligns successive tissue slides by finding correspondences between individually segmented muscle fibers using Fourier shape descriptors and computing the global rigid transformation using a voting scheme tolerant to mismatches. In the non-rigid stage we establish precise point correspondences using the normalized cross correlation metric and compute the non-rigid distortion using a polynomial transformation that minimizes the mean square distance between these control points.
[Show abstract][Hide abstract] ABSTRACT: The gold standard in follicular lymphoma (FL) diagnosis and prognosis is histopathological examination of tumor tissue samples. However, the qualitative manual evaluation is tedious and subject to considerable inter- and intra-reader variations. In this study, we propose an image analysis system for quantitative evaluation of digitized FL tissue slides. The developed system uses a robust feature space analysis method, namely the mean shift algorithm followed by a hierarchical grouping to segment a given tissue image into basic cytological components. We then apply further morphological operations to achieve the segmentation of individual cells. Finally, we generate a likelihood measure to detect candidate cancer cells using a set of clinically driven features. The proposed approach has been evaluated on a dataset consisting of 100 region of interest (ROI) images and achieves a promising 89% average accuracy in detecting target malignant cells.
[Show abstract][Hide abstract] ABSTRACT: Histopathological examination is one of the most important steps in evaluating prognosis of patients with neuroblastoma (NB). NB is a pediatric tumor of sympathetic nervous system and current evaluation of NB tumor histology is done according to the International Neuroblastoma Pathology Classification. The number of cells undergoing either mitosis or karyorrhexis (MK) plays an important role in this classification system. However, manual counting of such cells is tedious and subject to considerable inter- and intra-reader variations. A computer-assisted system may allow more precise results leading to more accurate prognosis in clinical practice. In this study, we propose an image analysis approach that operates on digitized NB histology samples. Based on the likelihood functions estimated from the samples of manually marked regions, we compute the probability map that indicates how likely a pixel belongs to an MK cell. Component-wise 2-step thresholding of the generated probability map provides promising results in detecting MK cells with an average sensitivity of 81.1% and 12.2 false positive detections on average.
No preview · Article · Sep 2009 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
[Show abstract][Hide abstract] ABSTRACT: Follicular lymphoma (FL) is the second most common type of non-Hodgkin's lymphoma. Manual histological grading of FL is subject to remarkable inter- and intra-reader variations. A promising approach to grading is the development of a computer-assisted system that improves consistency and precision. Correlating information from adjacent slides with different stain types requires establishing spatial correspondences between the digitized section pair through a precise non-rigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods. This study proposes a method for the automatic non-rigid registration of histological section images with different stain types. This method is based on matching high level features that are representative of small anatomical structures. This choice of feature provides a rich matching environment, but also results in a high mismatch probability. Matching confidence is increased by establishing local groups of coherent features through geometric reasoning. The proposed method is validated on a set of FL images representing different disease stages. Statistical analysis demonstrates that given a proper feature set the accuracy of automatic registration is comparable to manual registration.
Full-text · Article · Jun 2009 · Computer methods and programs in biomedicine
[Show abstract][Hide abstract] ABSTRACT: Neuroblastoma (NB) is one of the most frequently occurring cancerous tumors in children. The current grading evaluations for patients with this disease require pathologists to identify certain morphological characteristics with microscopic examinations of tumor tissues. Thanks to the advent of modern digital scanners, it is now feasible to scan cross-section tissue specimens and acquire whole-slide digital images. As a result, computerized analysis of these images can generate key quantifiable parameters and assist pathologists with grading evaluations. In this study, image analysis techniques are applied to histological images of haematoxylin and eosin (H&E) stained slides for identifying image regions associated with different pathological components. Texture features derived from segmented components of tissues are extracted and processed by an automated classifier group trained with sample images with different grades of neuroblastic differentiation in a multi-resolution framework. The trained classification system is tested on 33 whole-slide tumor images. The resulting whole-slide classification accuracy produced by the computerized system is 87.88%. Therefore, the developed system is a promising tool to facilitate grading whole-slide images of NB biopsies with high throughput.
No preview · Article · Jun 2009 · Pattern Recognition
[Show abstract][Hide abstract] ABSTRACT: We are developing a computer-aided prognosis system for neuroblastoma (NB), a cancer of the nervous system and one of the most malignant tumors affecting children. Histopathological examination is an important stage for further treatment planning in routine clinical diagnosis of NB. According to the International Neuroblastoma Pathology Classification (the Shimada system), NB patients are classified into favorable and unfavorable histology based on the tissue morphology. In this study, we propose an image analysis system that operates on digitized H&E stained whole-slide NB tissue samples and classifies each slide as either stroma-rich or stroma-poor based on the degree of Schwannian stromal development. Our statistical framework performs the classification based on texture features extracted using co-occurrence statistics and local binary patterns. Due to the high resolution of digitized whole-slide images, we propose a multi-resolution approach that mimics the evaluation of a pathologist such that the image analysis starts from the lowest resolution and switches to higher resolutions when necessary. We employ an offine feature selection step, which determines the most discriminative features at each resolution level during the training step. A modified k-nearest neighbor classifier is used to determine the confidence level of the classification to make the decision at a particular resolution level. The proposed approach was independently tested on 43 whole-slide samples and provided an overall classification accuracy of 88.4%.
Full-text · Article · Jun 2009 · Pattern Recognition
[Show abstract][Hide abstract] ABSTRACT: Follicular lymphoma (FL) is a cancer of lymph system and it is the second most common lymphoid malignancy in the western world.
Currently, the risk stratification of FL relies on histological grading method, where pathologists evaluate hematoxilin and
eosin (H&E) stained tissue sections under a microscope as recommended by the World Health Organization. This manual method
requires intensive labor in nature. Due to the sampling bias, it also suffers from inter- and intra-reader variability and
poor reproducibility. We are developing a computer-assisted system to provide quantitative assessment of FL images for more
consistent evaluation of FL. In this study, we proposed a statistical framework to classify FL images based on their histological
grades. We introduced model-based intermediate representation (MBIR) of cytological components that enables higher level semantic
description of tissue characteristics. Moreover, we introduced a novel color-texture analysis approach that combines the MBIR
with low level texture features, which capture tissue characteristics at pixel level. Experimental results on real follicular
lymphoma images demonstrate that the combined feature space improved the accuracy of the system significantly. The implemented
system can identify the most aggressive FL (grade III) with 98.9% sensitivity and 98.7% specificity and the overall classification
accuracy of the system is 85.5%.
No preview · Article · Apr 2009 · Journal of Signal Processing Systems
[Show abstract][Hide abstract] ABSTRACT: Neuroblastoma is one of the most common childhood cancers. We are developing an image analysis system to assist pathologists in their prognosis. Since this system operates on relatively large-scale images and requires sophisticated algorithms, computerised analysis takes a long time to execute. In this paper, we propose a novel approach to benefit from high memory bandwidth and strong floating-point capabilities of graphics processing units. The proposed approach achieves a promising classification accuracy of 99.4% and an execution performance with a gain factor up to 45 times compared to hand-optimised C++ code running on the CPU.
No preview · Article · Feb 2009 · International Journal of Data Mining and Bioinformatics
[Show abstract][Hide abstract] ABSTRACT: GPUs have recently evolved into very fast parallel co-processors capable of executing gen-eral purpose computations extremely efficiently. At the same time, multi-core CPUs evolution continued and today's CPUs have 4-8 cores. These two trends, however, have followed independent paths in the sense that we are aware of very few works that consider both devices cooperating to solve general computations. In this paper we investigate the coordinated use of CPU and GPU to improve efficiency of applications even further than using either device independently. We use Anthill runtime environment, a data-flow oriented framework in which applications are de-composed into a set of event-driven filters, where for each event, the runtime system can use either GPU or CPU for its processing. For evaluation, we use a histopathology application that uses image analysis techniques to classify tumor images for neuroblas-toma prognosis. Our experimental environment in-cludes dual and octa-core machines, augmented with GPUs and we evaluate our approach's performance for standalone and distributed executions. Our experiments show that a pure GPU opti-mization of the application achieved a factor of 15 to 49 times improvement over the single core CPU version, depending on the versions of the CPUs and GPUs. We also show that the execution can be further reduced by a factor of about 2 by using our runtime system that effectively choreographs the execution to run cooperatively both on GPU and on a single core of CPU. We improve on that by adding more cores, all of which were previously neglected or used ineffectively. In addition, the evaluation on a distributed environment has shown near linear scalability to multiple hosts.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we are proposing a novel automated method to recognize centroblast (CB) cells from non-centroblast (non-CB) cells for computer-assisted evaluation of follicular lymphoma tissue samples. The method is based on training and testing of a quadratic discriminant analysis (QDA) classifier. The novel aspects of this method are the identification of the CB object with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract color texture features. Both geometric and texture features are used to achieve the classification. Experimental results on real follicular lymphoma images demonstrate that the combined feature space improved the performance of the system significantly. The implemented method can identify centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56%.
Preview · Article · Jan 2009 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
[Show abstract][Hide abstract] ABSTRACT: In this paper, a novel color texture classification approach is introduced and applied to computer-assisted grading of follicular lymphoma from whole-slide tissue samples. The digitized tissue samples of follicular lymphoma were classified into histological grades under a statistical framework. The proposed method classifies the image either into low or high grades based on the amount of cytological components. To further discriminate the lower grades into low and mid grades, we proposed a novel color texture analysis approach. This approach modifies the gray level cooccurrence matrix method by using a nonlinear color quantization with self-organizing feature maps (SOFMs). This is particularly useful for the analysis of H&E stained pathological images whose dynamic color range is considerably limited. Experimental results on real follicular lymphoma images demonstrate that the proposed approach outperforms the gray level based texture analysis.
[Show abstract][Hide abstract] ABSTRACT: Neuroblastic Tumor (NT) is one of the most commonly occurring tumors in
children. Of all types of NTs, neuroblastoma is the most malignant tumor
that can be further categorized into undifferentiated (UD),
poorly-differentiated (PD) and differentiating (D) types, in terms of
the grade of pathological differentiation. Currently, pathologists
determine the grade of differentiation by visual examinations of tissue
samples under the microscope. However, this process is subjective and,
hence, may lead to intra- and inter-reader variability. In this paper,
we propose a multi-resolution image analysis system that helps
pathologists classify tissue samples according to their grades of
differentiation. The inputs to this system are color images of
haematoxylin and eosin (H&E) stained tissue samples. The complete
image analysis system has five stages: segmentation, feature
construction, feature extraction, classification and confidence
evaluation. Due to the large number of input images, both parallel
processing and multi-resolution analysis were carried out to reduce the
execution time of the algorithm. Our training dataset consists of 387
images tiles of size 512x512 in pixels from three whole-slide images. We
tested the developed system with an independent set of 24 whole-slide
images, eight from each grade. The developed system has an accuracy of
83.3% in correctly identifying the grade of differentiation, and it
takes about two hours, on average, to process each whole slide image.
Full-text · Article · Apr 2008 · Proceedings of SPIE - The International Society for Optical Engineering
[Show abstract][Hide abstract] ABSTRACT: Follicular Lymphoma (FL) is a cancer arising from the lymphatic system. Originating from follicle center B cells, FL is mainly comprised of centrocytes (usually middle-to-small sized cells) and centroblasts (relatively large malignant cells). According to the World Health Organization's recommendations, there are three histological grades of FL characterized by the number of centroblasts per high-power field (hpf) of area 0.159 mm2. In current practice, these cells are manually counted from ten representative fields of follicles after visual examination of hematoxylin and eosin (H&E) stained slides by pathologists. Several studies clearly demonstrate the poor reproducibility of this grading system with very low inter-reader agreement. In this study, we are developing a computerized system to assist pathologists with this process. A hybrid approach that combines information from several slides with different stains has been developed. Thus, follicles are first detected from digitized microscopy images with immunohistochemistry (IHC) stains, (i.e., CD10 and CD20). The average sensitivity and specificity of the follicle detection tested on 30 images at 2×, 4× and 8× magnifications are 85.5+/-9.8% and 92.5+/-4.0%, respectively. Since the centroblasts detection is carried out in the H&E-stained slides, the follicles in the IHC-stained images are mapped to H&E-stained counterparts. To evaluate the centroblast differentiation capabilities of the system, 11 hpf images have been marked by an experienced pathologist who identified 41 centroblast cells and 53 non-centroblast cells. A non-supervised clustering process differentiates the centroblast cells from noncentroblast cells, resulting in 92.68% sensitivity and 90.57% specificity.
Preview · Article · Mar 2008 · Proceedings of SPIE - The International Society for Optical Engineering
[Show abstract][Hide abstract] ABSTRACT: The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.