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

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%)

An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens

AbstractImmunofluorescence antinuclear antibody tests are important for diagnosis and management of autoimmune conditions; a key step that would benefit from reliable automation is the recognition of subcellular patterns suggestive of different diseases. We present a system to recognize such patterns, at cellular and specimen levels, in images of HEp-2 cells. Ensembles of SVMs were trained to classify cells into six classes based on sparse encoding of texture features with cell pyramids, capturing spatial, multi-scale structure. A similar approach was used to classify specimens into seven classes. Software implementations were submitted to an international contest hosted by ICPR 2014 (Performance Evaluation of Indirect Immunofluorescence Image Analysis Systems). Mean class accuracies obtained on heldout test data sets were 87.1% and 88.5% for cell and specimen classification respectively. These were the highest achieved in the competition, suggesting that our methods are state-of-the-art. We provide detailed descriptions and extensive experiments with various features and encoding methods

Pattern Classification of Human Epithelial Images

This project shows an important role to diagnosis autoimmune disorder which is by a comparative analysis on the most appropriate clustering technique for the segmentation and also to develop algorithm for positivity classification. In this project, there are four stages will be used to analyze pattern classification in human epithelial (HEp-2) images. First of all, image enhancement will take part in order to boost efficiency of algorithm by implementing some of the adjustment and filtering technique to increase the visibility of image. After that, the second stage will be the image segmentation by using most appropriate clustering technique. There will be a comparative analysis on clustering techniques for segmentation which are adaptive fuzzy c-mean and adaptive fuzzy moving k-mean. Then, for features extraction, by calculating the mean of each of the properties such as area, perimeter, major axis length, and minor axis length for each images. After that, will implementing a grouping based on properties dataset that has been calculated. Last but not least, from the mean of properties, it will classify into the pattern after ranging the value of mean properties of each of the pattern itself that has been done in classification stage

Classification of HEp-2 staining patterns in ImmunoFluorescence images. Comparison of Support Vector Machines and Subclass Discriminant Analysis strategies

nti-nuclear antibodies test is based on the visual evaluation of the intensity and staining pattern in HEp-2 cell slides by means of indirect immunofluorescence (IIF) imaging, revealing the presence of autoantibodies responsible for important immune pathologies. In particular, the categorization of the staining pattern is crucial for differential diagnosis, because it provides information about autoantibodies type. Their manual classification is very time-consuming and not very reliable, since it depends on the subjectivity and on the experience of the specialist. This motivates the growing demand for computer-aided solutions able to perform staining pattern classification in a fully automated way. In this work we compare two classification techniques, based respectively on Support Vector Machines and Subclass Discriminant Analysis. A set of textural features characterizing the available samples are first extracted. Then, a feature selection scheme is applied in order to produce different datasets, containing a limited number of image attributes that are best suited to the classification purpose. Experiments on IIF images showed that our computer-aided method is able to identify staining patterns with an average accuracy of about 91% and demonstrate, in this specific problem, a better performance of Subclass Discriminant Analysis with respect to Support Vector Machine

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)

MITOTIC HEP-2 CELL RECOGNIITON USING SUPPORT VECTOR MACHINE UNDER CLASS SKEW

A person with an autoimmune diseases will became hypersensitive to the surrounding that other normal person would usually not consider at all such as an allergy. This reaction happened when our immune system recognise our normal tissue as a dangerous foreign elements and proceed to attack them. The presence of antinuclear autoantibodies (ANA) in a patient serum can be detected by using the Indirect Immunofluorescence (IIF) image. By adding the mitotic cells into the well, the level of accuracy of the results achieved can be increased. The mitotic cells itself plays a crucial role in diagnosing an autoimmune diseases. This paper will focuses on the extracting the features of a mitotic HEp-2 cell in order to determine the presence of an ANA by noting the cells fluorescent-stained pattern, their intensity and also the presence of the mitotic cell itself. A skewed distribution of both mitotic and non-mitotic cells in the samples will also be considered to ensure the practicality of the project. To assist in the objectives, all the techniques used are explain in more detailed in this paper along with the result obtained by simulation from MATLAB for every steps from pre-processing to user interface menu. The procedures for the recognition of mitotic cells are image acquisition, pre-processing, segmentation, feature extraction and classification. The results obtained were tested using HEp-2 cell image datasets from MIVIA and from collaboration with Hospital Universiti Sains Malaysia (HUSM). The feature extractor used is the gray level co-occurrence matrix (GLCM) and classified using support vector machine (SVM) which will be presented in the RESULTS section

Segmentation Of Region Of Interest And Extraction Of Significant Features For Hep-2 Images

Human Epithelial type 2 (HEp-2) images are important in detecting the antinuclear autoantibody (ANA) in diagnosis of autoimmune disease in human body. Generally, HEp-2 cells can be classified into six main patterns, namely Centromere, Nucleolar, Homogeneous, Cytoplasmic, Fine Speckled and Coarse Speckled. However, in current technology, HEp-2 images can only be analysed manually by indirect immunofluorescence (IIF) test. The result of IIF test has very high variability and very dependent on the experience of physicists. Therefore, digitalize the IIF test becomes the new interest to researchers as well as in this research, where segmentation and features extraction of HEp-2 images will be focused. In segmentation of HEp-2 images, the current state-of-the-art techniques failed to provide a satisfied segmented result. Therefore, a combination of two conventional methods (i.e. Fuzzy C-Means (FCM) clustering and thresholding) has been proposed in this study. From the result, the segmented images are smoother, more consistent and with lesser noises compared to other state-of-the-art methods. In feature extraction stage, this study proposes to extract five features, which are Contrast, Energy, Correlation, Homogeneity, and Entropy. Based on the results obtained, the five proposed features can successfully differentiate the staining patterns of HEp-2 cells. In short, the proposed methods in this research have high capability to be introduced in hospital for detection of HEp-2 images for xix autoimmune disease. The proposed method has been proven with higher accuracy which can reduce the shortcoming of the existing IIF test