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Categorization and Segmentation of Intestinal Content and Pathological Frames in Wireless Capsule Endoscopy Images

S. Arivazhagan, W. Sylvia Lilly Jebarani, V. Jenifer Daisy


Wireless Capsule Endoscopy (WCE) is a modality that has become a very useful tool for diagnosing diseases with minimal discomfort for the patient. By using Wireless Capsule Endoscopy one can examine the digestive tract for Pathological conditions such as polyps, ulcers and Crohn’s disease. But in order to reduce the time several works have been proposed to remove the frames showing intestinal content. In this paper, we present a method for the robust detection and segmentation of intestinal content and pathological conditions in WCE images. Our proposed work consists of two modules. First, frames
containing intestinal content and pathological frames are detected by a Support Vector Machine (SVM) classifier using Local Binary Pattern (LBP), Edge Histogram Descriptor (EHD), Dominant Color Descriptor (DCD), Color Layout Descriptor (CLD) and Gray-Level Co-occurrence Matrix (GLCM) features. Second, intestinal content and pathological frames are segmented into bubbles, ulcer and polyp regions. In this proposed work, accuracy is improved with better classification segmentation over other previous methods.


Image segmentation, intestinal content, machine learning, polyps, ulcers, Wireless Capsule Endoscopy.

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