Prelimenary results of red lesion segmentation in WCE images

¹ LabSIV, Department of Computer Science, Faculty of Science, Ibn Zohr University, BP 8106, 80000 Agadir, Morocco
² Informatics and Applications Laboratory, Department of Computer Science, Faculty of Science, My Ismail University Meknès, Morocco
³ LabSIE, Department of Mathematics and Computer Science, multidisciplinary faculty, Ibn Zohr University, BP 638, 45000 Ouarzazate, Morocco

^* e-mail: charfisaid@gmail.com
^** e-mail: melansari@gmail.com
^*** e-mail: a.ellahyani@uiz.ac.ma
^**** e-mail: eljaafari.ilyas@gmail.com

Abstract

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.