An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

Abstract

• Unsupervised segmentation of polyps in colonoscopic images. • Superpixel level grouping of pixels using simple linear iterative clustering (SLIC). • Deployment of adaptive Markov random field (MRF) to stitch homogeneous regions with region adjacency graph (RAG). • Polyp segmentation efficiency of 60.77% is achieved in terms of dice co-efficient. In this article, an unsupervised image segmentation method is proposed to segment out polyps in endoscopic images. Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed. The polyps or abnormal regions show markedly different texture and color characteristics in contrast to normal tissues. In our method, the endoscope images are first over-segmented into superpixels . For final refinement, Local binary pattern (LBP) and color features are used in the adaptive MRF. The experimental results show that the proposed method can perfectly localize polyp regions, and it can give a mean dice value of 60.77% in a test set of 612 images taken from 29 different video sequences. Deep learning models require a huge number of training images for tweaking of network parameters. In this context, it is sometimes challenging to deploy a deep model for unseen medical datasets having many segmentation challenges, like complex background, specularity, non-uniform illuminations, etc. Also, manual labeling of a huge number of images is another challenge. That is why we adopted an unsupervised approach in this paper.