V. B. S. Prasath, Y. M. Kassim, Z. A. Oraibi, J.-B. Guiriec, A. Hafiane, G. Seetharaman, and K. Palaniappan

Proc. IEEE International Conference on Pattern Recognition (ICPR), 2016

hep-2 cell classification, cell segmentation, texture features, motif patterns, random forests, segmentation, biomedical

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Abstract

Human epithelial (HEp-2) cell specimens is obtained from indirect immunofluorescence (IIF) imaging for diagnosis and management of autoimmune diseases. Analysis of HEp2 cells is important and in this work we consider automatic cell segmentation and classification using spatial and texture pattern features and random forest classifiers. In this paper, we summarize our efforts in classification and segmentation tasks proposed in ICPR 2016 contest. For the cell level staining pattern classification (Task 1), we utilized texture features such as rotational invariant co-occurrence (RIC) versions of the well-known local binary pattern (LBP), median binary pattern (MBP), joint adaptive median binary pattern (JAMBP), and motif cooccurrence matrix (MCM) along with other optimized features. We report the classification results utilizing different classifiers such as the k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF). We obtained the best mean class accuracy of 94.29% for six cell classes with RIC-LBP combined with a RIC variant of MCM. For specimen level staining pattern classification (Task 2) we utilize a combination RIC-LBP with RF classifier and obtain 80% mean class accuracy (MCA) for seven classes. For cell segmentation (Task 4), we use our optimized multiscale spatial feature bank along with RF classifier for pixelwise labeling to achieve an F-measure of 84.26% for 1008 images.