Automatic Segmentation of Specular Reflections for Endoscopic Images Based on Sparse and Low-Rank Decomposition

Abstract

Endoscopy is a minimally invasive medical diagnostic procedure that is used to provide a realistic view of the surfaces of organs inside human body. Images taken during such procedures largely show tissues of human organs. Due to the presence of mucosa of the gastrointestinal tract or other characteristics of the human body, these surfaces usually have a glossy appearance showing specular reflections. For many image analysis algorithms, these distinct and bright visual mark can be a significant source of error. On other hand, these features can also be useful for image restoration and for the construction of 3D model of the organs. In this article, we propose a segmentation method of the specular regions based on sparse and low-rank decomposition using a robust PCA via accelerated proximal gradient algorithm. In contrast to the existing approaches, the proposed segmentation works without using colour image thresholds. Moreover, the proposed method presents more precise segmentation results represented by grayscale masks instead of binary masks.