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Level identification of brain MR images using histogram of a LBP variant
Abstract
Axial brain slices containing similar anatomical structures are retrieved using features derived from the histogram of Local binary pattern (LBP). A rotation invariant description of texture in terms of texture patterns and their strength is obtained with the incorporation of local variance to the LBP, called Modified LBP (MOD-LBP). In this paper, we compare Histogram based Features of LBP (HF/LBP), against Histogram based Features of MOD-LBP (HF/MOD-LBP) in retrieving similar axial brain images. We show that replacing local histogram with a local distance transform based similarity metric further improves the performance of MOD-LBP based image retrieval.
... This process, in which each expert's judgment is assigned a different weight based on its similarity to other experts' assessments, ensures that the aggregated assessments reflect both expert consensus and retain key individual insights. Fuzzy number defuzzification is extremely critical in the decision-making process [62], where fuzzy numbers aggregated through the SAM method are defuzzified through the centerof-area (CoV) method [63,64] and converted into specific probability values [65,66], which ultimately lead to the specific probability value of each root node, which is used to update the fuzzy logic parameters in the FBN, reflecting the degree of the impact of each risk factor on the quality of production, enabling the FBN to more accurately simulate the real risk situation and laying the foundation for further quality risk assessment. ...
This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform a probabilistic analysis of quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the “low professional level of designers”, “poor production refinement”, and “poor storage conditions for finished products” have a significant impact on quality risks. This study offers a scientific risk assessment tool designed to address the quality control challenges commonly experienced in the manufacturing of steel structural components. Identifying the critical risk factors that influence quality empowers actual production enterprises to develop risk management strategies and improvement measures in a more focused manner, thereby facilitating more effective resource allocation and risk prevention and control. Consequently, this approach has a significant impact on enhancing the overall production level and quality within the industry.
... These linguistic variables are then converted into specific fuzzy numbers, with detailed divisions of fuzzy probability intervals outlined in Table 3. Subsequently, the SAM (Bai et al., 2024;Hsi-Mei Hsu & Chen-Tung Chen, 1996;Lu et al., 2023) aggregates assessments from various experts. The defuzzification of fuzzy numbers, a crucial step in decision-making, is carried out using the CoA method (Runkler & Glesner, 1993;Varghese et al., 2012) along with formulas (4) and (5) (P. Chen et al., 2022;Onisawa, 1990;Van Laarhoven & Pedrycz, 1983). ...
This study aims to address quality issues in the production of prefabricated steel structural components for buildings by investigating challenges in quality risk assessment. It identifies key factors contributing to quality problems and establishes an evaluation index system. Traditional methods encounter limitations in handling uncertainty and conducting quantitative analysis. Therefore, the fuzzy Bayesian network (FBN) theory is utilized to perform probabilistic analysis on quality risks during the production phase. This research achieves a more accurate and dynamic risk assessment by integrating the strengths of fuzzy logic and Bayesian networks (BNs) and by utilizing expert knowledge, the similarity aggregation method (SAM), and the noisy-OR gate model. The study reveals that factors such as the "low professional level of designers," "poor production refinement," and "poor storage conditions for finished products" have a significant impact on quality risks. This research offers a novel risk assessment tool for steel structural component production, effectively assisting enterprises in identifying potential risks, formulating risk reduction strategies, and enhancing production quality.
... The aggregated TIFN Ã = (a, b, c; a ′ , b, c ′ ) of the BE can be defuzzied by the center of area method [68]. The processes of transforming TIFN into FFP through defuzzification are divided into two stages: converting the TIFN into a fuzzy possibility score (FPS), and then converting the FPS into FFP. ...
... However, this method faces noise influence when employed on the image as a whole. Varghese et al. presented an extended version of LBP, named modified LBP (MOD-LBP), and showed its application in the level identification of brain MR images [128]. When compared with original LBP, using histogram-based features, Varghese et al. found MOD-LBP to be two times better. ...
Face detection, which is an effortless task for humans, is complex to perform on machines. The recent veer proliferation of computational resources is paving the way for frantic advancement of face detection technology. Many astutely developed algorithms have been proposed to detect faces. However, there is little attention paid in making a comprehensive survey of the available algorithms. This paper aims at providing fourfold discussions on face detection algorithms. First, we explore a wide variety of the available face detection algorithms in five steps, including history, working procedure, advantages, limitations, and use in other fields alongside face detection. Secondly, we include a comparative evaluation among different algorithms in each single method. Thirdly, we provide detailed comparisons among the algorithms epitomized to have an all-inclusive outlook. Lastly, we conclude this study with several promising research directions to pursue. Earlier survey papers on face detection algorithms are limited to just technical details and popularly used algorithms. In our study, however, we cover detailed technical explanations of face detection algorithms and various recent sub-branches of the neural network. We present detailed comparisons among the algorithms in all-inclusive and under sub-branches. We provide the strengths and limitations of these algorithms and a novel literature survey that includes their use besides face detection.
... The above measures are invariant to monotonic gray level changes because just the sign of the differences only are considered instead of their exact values. In order to account for the changes in contrast an grayscale shift with respect to the window taken, the definition of the LBP is modified [9] as shown in ...
decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users' feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved. Keywords—Local Binary pattern (LBP), Modified Local Binary pattern (MOD-LBP), T1 and T2 weighted images, Moment features.
Invention of the digital camera and also cell phones with powerful cameras with moderate and low pricing system has given the common man the privilege to capture his world in pictures anywhere, at any time, and conveniently share them with others. This has resulted the generation of volumes of images. These factors have created numerous possibilities and finally created interest among the researchers towards the design of an efficient and accurate Content Based Information Retrieval (CBIR) system. That's why new technological advances and growth in CBIR has been unquestionably rapid during the last five years. Various face recognition methods are derived using local features, and among them the Local Binary Pattern (LBP) approach is very famous. The basic disadvantage of these methods is they completely fail in representing features derived from large or macro structures or regions, which are very much essential for faces. To address this present paper proposes a median based multi region LBP. The proposed median based multi region LBP, initially divides the facial image in to non-overlapped regions of size 5 × 5. LBP values are evaluated by dividing the region in to sub regions of size 3 × 3. The 9 sub-region LBP values are arranged in the sorted manner and the median LBP code is considered as the feature vector for the region. The present paper also proposes the minimum and maximum based regional LBP methods for efficient image retrieval. To overcome the noise and illumination effect the proposed method initially applied DOG preprocessing method with gamma correction. The proposed method is applied on FG-NET and Goggle databases for efficient facial image retrieval. The experimental results indicate the efficiency of the proposed method.
Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.
This paper presents the MultiWaveMed system, which is a new software allowing to index and retrieve medical images through the comparison of their texture features. The features are extracted by wavelet transforms, and are organized in feature vectors. The system extracts the image texture features, computes the distance between the query image to all images in the database, through the comparison of their features, and retrieve de n most similar images regarding this kind of feature. The proposed system has implemented both Daubechies and Gabor wavelets. The feature vectors extracted from the images are used to organize the images through access methods, which are the basis to perform the query-by-content operations over the images. The focus of this paper is to show the utility of the wavelet transforms on medical image characterization and their suitability for image indexing and retrieval.
The aging population and the growing amount of medical data have increased the need for automated tools in the neurology departments. Although the researchers have been developing computerized methods to help the medical expert, these efforts have primarily emphasized to improve the effectiveness in single patient data, such as computing a brain lesion size. However, patient-to-patient comparison that should help improve diagnosis and therapy has not received much attention. To this effect, this paper introduces a fast and robust region-of-interest retrieval method for brain MR images. We make the following various contributions to the domains of brain MR image analysis, and search and retrieval system: 1) we show the potential and robustness of local structure information in the search and retrieval of brain MR images; 2) we provide analysis of two complementary features, local binary patterns (LBPs) and Kanade-Lucas-Tomasi feature points, and their comparison with a baseline method; 3) we show that incorporating spatial context in the features substantially improves accuracy; and 4) we automatically extract dominant LBPs and demonstrate their effectiveness relative to the conventional LBP approach. Comprehensive experiments on real and simulated datasets revealed that dominant LBPs with spatial context is robust to geometric deformations and intensity variations, and have high accuracy and speed even in pathological cases. The proposed method can not only aid the medical expert in disease diagnosis, or be used in scout (localizer) scans for optimization of acquisition parameters, but also supports low-power handheld devices.
Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered.
Presents a review of 200 references in content-based image
retrieval. The paper starts with discussing the working conditions of
content-based retrieval: patterns of use, types of pictures, the role of
semantics, and the sensory gap. Subsequent sections discuss
computational steps for image retrieval systems. Step one of the review
is image processing for retrieval sorted by color, texture, and local
geometry. Features for retrieval are discussed next, sorted by:
accumulative and global features, salient points, object and shape
features, signs, and structural combinations thereof. Similarity of
pictures and objects in pictures is reviewed for each of the feature
types, in close connection to the types and means of feedback the user
of the systems is capable of giving by interaction. We briefly discuss
aspects of system engineering: databases, system architecture, and
evaluation. In the concluding section, we present our view on: the
driving force of the field, the heritage from computer vision, the
influence on computer vision, the role of similarity and of interaction,
the need for databases, the problem of evaluation, and the role of the
semantic gap
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related fields. In this article, we survey almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and in the process discuss the spawning of related subfields. We also discuss significant challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Local or global rotation invariant feature extraction has been widely used in texture classification. Local invariant features, e.g. local binary pattern (LBP), have the drawback of losing global spatial information, while global features preserve little local texture information. This paper proposes an alternative hybrid scheme, globally rotation invariant matching with locally variant LBP texture features. Using LBP distribution, we first estimate the principal orientations of the texture image and then use them to align LBP histograms. The aligned histograms are then in turn used to measure the dissimilarity between images. A new texture descriptor, LBP variance (LBPV), is proposed to characterize the local contrast information into the one-dimensional LBP histogram. LBPV does not need any quantization and it is totally training-free. To further speed up the proposed matching scheme, we propose a method to reduce feature dimensions using distance measurement. The experimental results on representative databases show that the proposed LBPV operator and global matching scheme can achieve significant improvement, sometimes more than 10% in terms of classification accuracy, over traditional locally rotation invariant LBP method.
In order to improve the retrieval accuracy of content-based image retrieval systems, research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the ‘semantic gap’ between the visual features and the richness of human semantics. This paper attempts to provide a comprehensive survey of the recent technical achievements in high-level semantic-based image retrieval. Major recent publications are included in this survey covering different aspects of the research in this area, including low-level image feature extraction, similarity measurement, and deriving high-level semantic features. We identify five major categories of the state-of-the-art techniques in narrowing down the ‘semantic gap’: (1) using object ontology to define high-level concepts; (2) using machine learning methods to associate low-level features with query concepts; (3) using relevance feedback to learn users’ intention; (4) generating semantic template to support high-level image retrieval; (5) fusing the evidences from HTML text and the visual content of images for WWW image retrieval. In addition, some other related issues such as image test bed and retrieval performance evaluation are also discussed. Finally, based on existing technology and the demand from real-world applications, a few promising future research directions are suggested.
We have witnessed great interest and a wealth of promise in content-based image retrieval as an emerging technology. While the last decade laid foundation to such promise, it also paved the way for a large number of new techniques and systems, got many new people involved, and triggered stronger association of weakly related elds. In this paper, we survey almost 300 key theoret- ical and empirical contributions in the current decade related to image retrieval and automatic image annotation, and discuss the spawning of related sub-elds in the process. We also discuss signican t challenges involved in the adaptation of existing image retrieval techniques to build systems that can be useful in the real-world. In retrospect of what has been achieved so far, we also conjecture what the future may hold for image retrieval research.
Image reference databases (IRDBs) are a recurrent research topic in medical imaging. Most IRDBs are designed to help experienced physicians in diagnostic tasks and require that users have prior extensive knowledge of the field for their use to be fruitful. Therefore, the educational potential of such image collections cannot be exploited thoroughly. In this paper we propose an image-indexing method to extend the functionalities of an existing medical IRDB and allow for its use in educational applications, as well as in computer-assisted diagnosis. Our method, based on the Kahrunen-Leève transform, has been used to develop a content-based search engine for tomographic image databases on which we are presently experimenting and which we aim to integrate into a working radiological IRDB installed at the University of Florence. Results achieved in our preliminary tests are also reported.
The capacity to retrieve images containing objects with shapes similar to a query shape is desirable in medical image databases. We propose a similarity measure and an indexing mechanism for non-rigid comparison of shape which adds this capability to image databases. The (dis-)similarity measure is based on the observations that: (1) the geometry of the same organ in different subjects is not related by a strictly rigid transformation; and (2) the orientation of the organ plays a key role in comparing shape. We propose a similarity measure that computes a non-rigid mapping between curves and uses this mapping to compare oriented shape. We also show how KD-trees can index curves so that retrieval with our similarity measure is efficient. Experiments with real-world data from a database of magnetic resonance images are provided.
Categorization of medical images means selecting the appropriate class for a given image out of a set of pre-defined categories. This is an important step for data mining and content-based image retrieval (CBIR). So far, published approaches are capable to distinguish up to 10 categories. In this paper, we evaluate automatic categorization into more than 80 categories describing the imaging modality and direction as well as the body part and biological system examined. Based on 6231 reference images from hospital routine, 85.5% correctness is obtained combining global texture features with scaled images. With a frequency of 97.7%, the correct class is within the best ten matches, which is sufficient for medical CBIR applications.
This paper evaluates the performance both of some texture measures
which have been successfully used in various applications and of some
new promising approaches. For classification a method based on Kullback
discrimination of sample and prototype distributions is used. The
classification results for single features with one-dimensional feature
value distributions and for pairs of complementary features with
two-dimensional distributions are presented