Robust Fuzzy C-Mean algorithm for Segmentation and analysis of Cytological images
Chinmoy Nath, Jyotismita Talukdar, Pranhari Talukdar · 2012
In this paper, we are proposing a method for segmentation of PAP (Papinocolaou) smear images using Fuzzy C-Mean Algorithm (FCM) and analysis of the segmented images based on shape and size criteria. The traditional FCM algorithm used in the present study is modified by replacing the Euclidean distance metric by Mahalanobis distance metric. Further, the computation of the cluster center is modified by including the gray level distribution i.e. the histogram of the image. For shape and size analysis, the cell nuclei distribution based on area, compactness and eccentricity of the cell nuclei are computed. It is found that the proposed Mahalanobis distance metric enhances the ability of FCM algorithm to detect clusters of arbitrary shapes. Further, the inclusion of histogram in the computation of cluster center reduces the computation time significantly. The shape and size analysis provides more specific information for classification of the images more accurately and efficiently.