Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.Comment: arXiv admin note: substantial text overlap with arXiv:1304.126

HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test called indirect immunofluorescence (IIF) will be carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. The outcome of the test includes observing fluorescence intensity of the sample and classifying the staining pattern of the cell. Current method of analysing the results is limited to subjective factors such as experience and skill of the medical experts. The results obtained from the visual analysis are debatable as it is inconsistent. Thus, there is a need for an automated recognition system to reduce the variability and increase the reliability of the test results. Automated system also saves time and cost as the system is able to process large amount of image data at one time. This project proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. This method is applied to the data set of the ICPR 2012 contest in which each cell has been manually segmented and annotated by specialists. The textural features extracted are based on the first-order statistics and second-order statistics computed from grey level co-occurrence matrices (GLCM). The first-order statistics features are mean, standard deviation and entropy while the features extracted by GLCM are contrast, correlation, energy and homogeneity. The extracted features will then be used as an input parameter to classify the staining pattern of the HEp-2 cell images by using Fuzzy Logic. The staining patterns are divided into five categories; homogeneous, nucleolar, centromere, fine speckled and coarse speckled. A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. The algorithm gives a mean accuracy of 84% out of 125 test images

HEP-2 CELL IMAGES CLASSIFICATION BASED ON STATISTICAL TEXTURE ANALYSIS AND FUZZY LOGIC

Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test called indirect immunofluorescence (IIF) will be carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. The outcome of the test includes observing fluorescence intensity of the sample and classifying the staining pattern of the cell. Current method of analysing the results is limited to subjective factors such as experience and skill of the medical experts. The results obtained from the visual analysis are debatable as it is inconsistent. Thus, there is a need for an automated recognition system to reduce the variability and increase the reliability of the test results. Automated system also saves time and cost as the system is able to process large amount of image data at one time. This project proposes a pattern recognition algorithm consisting of statistical methods to extract seven textural features from the HEp-2 cell images followed by classification of staining patterns by using fuzzy logic. This method is applied to the data set of the ICPR 2012 contest in which each cell has been manually segmented and annotated by specialists. The textural features extracted are based on the first-order statistics and second-order statistics computed from grey level co-occurrence matrices (GLCM). The first-order statistics features are mean, standard deviation and entropy while the features extracted by GLCM are contrast, correlation, energy and homogeneity. The extracted features will then be used as an input parameter to classify the staining pattern of the HEp-2 cell images by using Fuzzy Logic. The staining patterns are divided into five categories; homogeneous, nucleolar, centromere, fine speckled and coarse speckled. A working classification algorithm is developed by using MATLAB and the Fuzzy Logic Toolbox to differentiate and classify the staining pattern of HEp-2 cell images. The algorithm gives a mean accuracy of 84% out of 125 test images

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF)method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of aCAD(Computer AidedDetection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)

Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis

Texture can be broadly stated as spatial variation of image intensities. Texture analysis and classification is a well researched area for its importance to many computer vision applications. Consequently, much research has focussed on deriving powerful and efficient texture descriptors. Local binary patterns (LBP) and its variants are simple yet powerful texture descriptors. LBP features describe the texture neighbourhood of a pixel using simple comparison operators, and are often calculated based on varying neighbourhood radii to provide multi-resolution texture descriptions. A comprehensive evaluation of different LBP variants on a common benchmark dataset is missing in the literature. This thesis presents the performance for different LBP variants on texture classification and retrieval tasks. The results show that multi-scale local binary pattern variance (LBPV) gives the best performance over eight benchmarked datasets. Furthermore, improvements to the Dominant LBP (D-LBP) by ranking dominant patterns over complete training set and Compound LBP (CM-LBP) by considering 16 bits binary codes are suggested which are shown to outperform their original counterparts. The main contribution of the thesis is the introduction of multi-dimensional LBP features, which preserve the relationships between different scales by building a multi-dimensional histogram. The results on benchmarked classification and retrieval datasets clearly show that the multi-dimensional LBP (MD-LBP) improves the results compared to conventional multi-scale LBP. The same principle is applied to LBPV (MD-LBPV), again leading to improved performance. The proposed variants result in relatively large feature lengths which is addressed using three different feature length reduction techniques. Principle component analysis (PCA) is shown to give the best performance when the feature length is reduced to match that of conventional multi-scale LBP. The proposed multi-dimensional LBP variants are applied for medical image analysis application. The first application is nailfold capillary (NC) image classification. Performance of MD-LBPV on NC images is highest, whereas for second application, HEp-2 cell classification, performance of MD-LBP is highest. It is observed that the proposed texture descriptors gives improved texture classification accuracy

Local and deep texture features for classification of natural and biomedical images

Developing efficient feature descriptors is very important in many computer vision applications including biomedical image analysis. In the past two decades and before the popularity of deep learning approaches in image classification, texture features proved to be very effective to capture the gradient variation in the image. Following the success of the Local Binary Pattern (LBP) descriptor, many variations of this descriptor were introduced to further improve the ability of obtaining good classification results. However, the problem of image classification gets more complicated when the number of images increases as well as the number of classes. In this case, more robust approaches must be used to address this problem. In this thesis, we address the problem of analyzing biomedical images by using a combination of local and deep features. First, we propose a novel descriptor that is based on the motif Peano scan concept called Joint Motif Labels (JML). After that, we combine the features extracted from the JML descriptor with two other descriptors called Rotation Invariant Co-occurrence among Local Binary Patterns (RIC-LBP) and Joint Adaptive Medina Binary Patterns (JAMBP). In addition, we construct another descriptor called Motif Patterns encoded by RIC-LBP and use it in our classification framework. We enrich the performance of our framework by combining these local descriptors with features extracted from a pre-trained deep network called VGG-19. Hence, the 4096 features of the Fully Connected 'fc7' layer are extracted and combined with the proposed local descriptors. Finally, we show that Random Forests (RF) classifier can be used to obtain superior performance in the field of biomedical image analysis. Testing was performed on two standard biomedical datasets and another three standard texture datasets. Results show that our framework can beat state-of-the-art accuracy on the biomedical image analysis and the combination of local features produce promising results on the standard texture datasets.Includes bibliographical reference