Hep-2 Cell Classification And Segmentation Using Motif Texture Patterns And Spatial Features With Random Forests

Human epithelial (HEp-2) cell specimens are 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 labels (ML) 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 accuracy of 94.26% for six cell classes with RIC-LBP combined with a motif pattern co-occurrence labels (MCL). For specimen level staining pattern classification (Task 2) we utilize a combination RIC-LBP with RF classifier and obtain 80% accuracy for seven classes. For cell segmentation (Task 4), we use our optimized multiscale spatial feature bank along with RF classifier for pixel-wise labeling to achieve an F-measure of 84.26% for 1008 images.