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

An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing

Rheumatoid Arthritis (RA) is an autoimmune disease that causes chronic pain, stiffness, redness or loss of function in the joints. Other than early diagnosis, there is yet a cure available for RA. Diseases with similar symptoms such as lupus, osteoarthritis, gout cause difficulty in diagnosing RA. Currently, indirect immunofluorescence (IIF) test performed to identify ANA in Hep-2 cells. Thus, image processing techniques vital to make diagnosis more efficient, accurate and less time-consuming. For this project standardized staining pattern classifier to be designed by using image processing techniques. Current manual techniques has limited accuracy and time consuming. In IFF procedures, unsuitable microscope to read Hep-2 cell slides, or photo bleaching effect where cells bleached extremely in short period of time are disadvantages. Another downside is test results being subject to change with experts knowledge and years of experience. These factors lead to low accuracy and it becomes a lengthy process due to large number of images. Out of five types of staining patterns nucleolar and centromere share similar visual appearance and the same is true to homogeneous, fine-speckled, coarse-speckled patterns. This is one of the major factors affecting classification accuracy due to results being subjective. In this research, First and Second Order Statistics Feature Extraction, Mamdani Fuzzy Logic Classification methods utilized to develop automatic detection system for RA with the help of Matlab R2012b, Fuzzy Logic Toolbox, and Image Processing Toolbox. The algorithm tested on the publicly available Mivia Hep-2 Cell image dataset. Fuzzy logic classified 85 out of 250 images wrongly. It has 66% accuracy. The images obtained from MIVIA dataset has been manually segmented to cell level from the image level. Developing an automated segmentation algorithm might give better results

HEP-2 CELL IMAGES FLUORESCENCE INTENSITY CLASSIFICATION TO DETERMINE POSITIVITY BASED ON NEURAL NETWORK AMIN

Nowadays, the recommended method for detection of anti-nuclear auto-antibodies is by using Indirect Immunofluorescence (IIF). The increasing of test demands on classification of Hep-2 cell images force the physicians to carry out the test faster, resulting bad quality results. IIF diagnosis requires estimating the fluorescence intensity of the serum and this will be observed. As there are subjective and inter/intra laboratory perception of the results, the development of computer-aided diagnosis (CAD) tools is used to support the decision. In this report, we propose the classification technique based on Artificial Neural Network (ANN) that can classify the Hep-2 cell images into 3 classes namely positive, negative and intermediate,specifically to determine the presence of antinuclear autoantibodies (ANA)

Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors

The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to identify the existence of various diseases. A hallmark method for identifying the presence of ANAs is the Indirect Immunofluorescence method on Human Epithelial (HEp-2) cells, due to its high sensitivity and the large range of antigens that can be detected. However, the method 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. In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier. We evaluate the performance of several variants of the descriptor on two publicly available datasets: ICPR HEp-2 cell classification contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the first time codebook-based descriptors are applied and studied in this domain. Experiments show that the proposed system has consistent high performance and is more robust than two recent CAD systems

ENHANCEMENT ANALYSIS OF IMMUNE FLUORESCENT CELL IMAGES

There are different patterns of immune fluorescence cells, which serve in determining different autoimmune disease. Hence, clearly identifying the features of the figures in the image will assist in automating the classification of these patterns. This project aims to enhance the quality of the Hep2-cell images obtained from Indirect Immune Fluorescence (IIF) Test. The enhancement of the quality in this project will be focused on enhancing the contrast, reducing the noise, and sharpening the edges of images. This enhancement will have a real serious impact on the stages coming after, which are patterns recognition and automatic classification. Creating an automatic battern classification system will improve the diagnostic process of the autoimmune disease instead of handling it manually. Consequently, many disadvantages of the manual interpretation can be overcome, such as level of expertise, time consuming and prone to mistakes. This research analyzed the performance of three enhancement approaches namely wavelet transform filter, diffusion filter, and wavelet transform filter combined with diffusion filter. The combination of wavelet transform filter with diffusion filter produced better result. However, the diffusion filter produced best result among all the three enhancement approach of the indirect immune fluorescence images. The recommendation for the future work is to explore an automatic determination of noise variance in the image when wavelet transform filter is being applied

“SEGMENTATION OF ANTI NEUTROPHIL CYTOPLASMIC ANTIBODIES (ANCA) IMAGES BASED ON WATERSHED AND WAVELET”

Autoimmune disease is a type of disease where immune system unable to tell between the good side and bad side which lead to the misguided attack on the healthy cells and tissues. Autoimmune disease can be classified to more than 80 types depending on the affected area. The test also varies according to the suspected type of disease. Some examples of the test are Enzyme-Linked Immunosorbent Assay (ELISA) test, Indirect Immunofluorescence (IIF) test of Antinuclear Antibody (ANA) by using HeP-2 Cells and IIF test for Anti Neutrophil Cytoplasmic Antibodies (ANCA). However in this project, author only focus on the ANCA images with two major staining patterns which are P-ANCA and C-ANCA. Currently the positivity of the images depends solely on the experience of the physician which led to variety of result and lack of reliability. Besides the time to get the result is time consuming. Thus an automatic classification system has been developed to overcome the manual process. The vital process inside the automatic system is the segmentation part. Many researchers suggest different techniques of segmentation to segment the ANCA images before being further processed. In this research, author focus on Watershed technique to segment the ANCA images by implementing the algorithm in Matlab. Author use Wavelet transform to suppress noise to avoid from over segmentation of the ANCA images. Using Rand Index method, the result of segmentations is verified. Combination of Watershed and Wavelet transform gives a very promising result. Recommendation for future work is to explore on automatic determination of noise variance inside images

An Intelligent Detection System for Rheumatoid Arthritis (RA) Disease using Image Processing

Rheumatoid Arthritis (RA) is an autoimmune disease that causes chronic pain, stiffness, redness or loss of function in the joints. Other than early diagnosis, there is yet a cure available for RA. Diseases with similar symptoms such as lupus, osteoarthritis, gout cause difficulty in diagnosing RA. Currently, indirect immunofluorescence (IIF) test performed to identify ANA in Hep-2 cells. Thus, image processing techniques vital to make diagnosis more efficient, accurate and less time-consuming. For this project standardized staining pattern classifier to be designed by using image processing techniques. Current manual techniques has limited accuracy and time consuming. In IFF procedures, unsuitable microscope to read Hep-2 cell slides, or photo bleaching effect where cells bleached extremely in short period of time are disadvantages. Another downside is test results being subject to change with experts knowledge and years of experience. These factors lead to low accuracy and it becomes a lengthy process due to large number of images. Out of five types of staining patterns nucleolar and centromere share similar visual appearance and the same is true to homogeneous, fine-speckled, coarse-speckled patterns. This is one of the major factors affecting classification accuracy due to results being subjective. In this research, First and Second Order Statistics Feature Extraction, Mamdani Fuzzy Logic Classification methods utilized to develop automatic detection system for RA with the help of Matlab R2012b, Fuzzy Logic Toolbox, and Image Processing Toolbox. The algorithm tested on the publicly available Mivia Hep-2 Cell image dataset. Fuzzy logic classified 85 out of 250 images wrongly. It has 66% accuracy. The images obtained from MIVIA dataset has been manually segmented to cell level from the image level. Developing an automated segmentation algorithm might give better results

HEP-2 CELL IMAGES FLUORESCENCE INTENSITY CLASSIFICATION TO DETERMINE POSITIVITY BASED ON NEURAL NETWORK AMIN

Nowadays, the recommended method for detection of anti-nuclear auto-antibodies is by using Indirect Immunofluorescence (IIF). The increasing of test demands on classification of Hep-2 cell images force the physicians to carry out the test faster, resulting bad quality results. IIF diagnosis requires estimating the fluorescence intensity of the serum and this will be observed. As there are subjective and inter/intra laboratory perception of the results, the development of computer-aided diagnosis (CAD) tools is used to support the decision. In this report, we propose the classification technique based on Artificial Neural Network (ANN) that can classify the Hep-2 cell images into 3 classes namely positive, negative and intermediate,specifically to determine the presence of antinuclear autoantibodies (ANA)

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