Automatic segmentation and classification of mitotic cell nuclei in histopathology images based on Active Contour Model

Abstract

Segmentation accuracy determines the success or failure of computerized analysis procedure in biomedical applications. This paper aims to develop a unique segmentation technique to identify mitotic nuclei from microscopy images of breast histopathology slides. The process involves detection and classification of cell nuclei based on computed features. The proposed method uses Active Contour Model for segmentation of cell nuclei and two versatile classifiers such as Support Vector Machine (SVM) and Random Forest (RF) for classification stage. Segmentation stage provides an accuracy of 95% for cell nuclei. This technique uses a single color channel and a reduced feature set for the whole process. Classification performance is evaluated in terms of sensitivity, specificity, accuracy and F-score measures. Analysis results showed good detection accuracy for RF classifier compared to SVM.