Superpixel-based classification of gastric chromoendoscopy images

Conference paper


Boschetto, D and Grisan, E (2017). Superpixel-based classification of gastric chromoendoscopy images. SPIE Medical Imaging. Orlando, FL, USA 11 - 16 Feb 2017 SPIE. doi:10.1117/12.2254187
AuthorsBoschetto, D and Grisan, E
TypeConference paper
Abstract

Chromoendoscopy (CH) is a gastroenterology imaging modality that involves the staining of tissues with methylene blue, which reacts with the internal walls of the gastrointestinal tract, improving the visual contrast in mucosal surfaces and thus enhancing a doctor’s ability to screen precancerous lesions or early cancer. This technique helps identify areas that can be targeted for biopsy or treatment and in this work we will focus on gastric cancer detection. Gastric chromoendoscopy for cancer detection has several taxonomies available, one of which classifies CH images into three classes (normal, metaplasia, dysplasia) based on color, shape and regularity of pit patterns. Computer-assisted diagnosis is desirable to help us improve the reliability of the tissue classification and abnormalities detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. We propose the exploitation of a first unsupervised segmentation via superpixel, which groups pixels into perceptually meaningful atomic regions, used to replace the rigid structure of the pixel grid. For each superpixel, a set of features is extracted and then fed to a random forest based classifier, which computes a model used to predict the class of each superpixel. The average general accuracy of our model is 92.05% in the pixel domain (86.62% in the superpixel domain), while detection accuracies on the normal and abnormal class are respectively 85.71% and 95%. Eventually, the whole image class can be predicted image through a majority vote on each superpixel’s predicted class. © 2017 SPIE.

Keywordschromoendoscopy; classification; gastric cancer; superpixel
Year2017
JournalProc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101340W
PublisherSPIE
Journal citation10134
Digital Object Identifier (DOI)doi:10.1117/12.2254187
Web address (URL)https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020251985&doi=10.1117%2f12.2254187&partnerID=40&md5=a710f003ba3d25d16124dc893b57252e
Accepted author manuscript
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CC BY-NC 4.0
File Access Level
Open
Publication dates
Print03 Mar 2017
Publication process dates
Accepted05 Oct 2016
Deposited27 Jan 2020
ISBN9781510607132
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https://openresearch.lsbu.ac.uk/item/88xv3

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