Nuclei Segmentation using a Level Set Active Contour Method and Spatial Fuzzy C-means Clustering

Digitized histology images are analyzed by expert pathologists in one of several approaches to assess precervical cancer conditions such as cervical intraepithelial neoplasia (CIN). Many image analysis studies focus on detection of nuclei features to classify the epithelium into the CIN grades. The current study focuses on nuclei segmentation based on level set active contour segmentation and fuzzy c-means clustering methods. Logical operations applied to morphological post-processing operations are used to smooth the image and to remove non-nuclei objects. On a 71-image dataset of digitized histology images (where the ground truth is the epithelial mask which helps in eliminating the non epithelial regions), the algorithm achieved an overall nuclei segmentation accuracy of 96.47%. We propose a simplified fuzzy spatial cost function that may be generally applicable for any n-class clustering problem of spatially distributed objects.