Histo-pathological image analysis using OS-FCM and level sets

Abstract

Malignant melanomas are the most serious form of skin cancer accounting for the majority of skin cancer related deaths. Histo-pathological images of skin tissues are analyzed for detecting various types of melanomas. The automatic analysis of these images can greatly facilitate the diagnosis task for dermato-pathologists. The first and foremost step in automatic histo-pathological image analysis is to accurately segment the images into dermal and epidermal layers along with segmenting other tissues structures such as nests and melanocytic cells which indicate the presence of cancer. In this paper, we present a novel technique for segmenting the dermalepidermal junction based on color features which are initially clustered using the Orientation Sensitive Fuzzy C-means algorithm (OS-FCM) and later refined with level set based algorithms. A few novel parameters which define the architecture of the dermis are then extracted. Experimental results on a small database of skin tissue images show the efficacy of the proposed methodology in differentiating between melanomas and naevi.

Fuzzy C Mean Thresholding based Level Set for Automated Segmentation of Skin Lesions

“…However, intensive computational requirements and regulation of controlling parameters make it a complex and time consuming method. To overcome such shortcomings, fuzzy clustering has been used to facilitate the LS segmentation for automatic segmentation of ultrasound, computed tomography and magnetic resonance imaging [17][18][19]. Fuzzy C mean (FCM) based thresholding [20] and LS algorithm are different computational models that have been applied individually for segmentation of dermoscopic images [21,22].…”