Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/326842
Type: Artigo
Title: Prototype System For Feature Extraction, Classification And Study Of Medical Images
Author: Oliva
Jefferson Tales; Lee
Huei Diana; Spolaor
Newton; Coy
Claudio Saddy Rodrigues; Wu
Feng Chung
Abstract: Colonoscopy exam images are useful to identify diseases, such as the colorectal cancer, which is one of the most common cancers worldwide. Computational image analysis and machine learning techniques can assist experts to identify abnormalities in these images. In this work, we present and evaluate MIAS 3.0, which aims to help experts to study and analyze colon tissue images. To do so, the system initially extracts features from these images. Currently, Amadasum, Haralick and Laws texture descriptors are supported. Then, the described images are classified into normal or abnormal images. In this version, J48, nearest neighbor, backpropagation based on multilayer perceptron, naive Bayes, and support vector machine classification algorithms are implemented. MIAS was developed with open source technologies using a software engineering approach to improve flexibility and maintainability. In this work, MIAS was quantitatively assessed by its application in a set of 134 tissue image fragments. The classifiers built from this set were compared according to the cross-validation and contingency table strategies. Also, the system was qualitatively evaluated using 12 heuristics by twelve volunteers from Health and Exact Sciences. The issues found were categorized according to Rolf Molich's severity scale. As a result, the J48 classifier achieved the highest sensitivity (85.07%) and reasonable average error (18.68%). In the qualitative evaluation, 61.26% of the issues found were not considered serious. These assessments suggest that MIAS can be useful to assist domain experts with minimum knowledge in informatics to conduct more complete studies of medical images, by identifying patterns regarding different abnormalities. (C) 2016 Published by Elsevier Ltd.
Subject: Image Analysis
Texture
Machine Learning
Prototyping
Artificial Intelligence
Medical Information Systems
Editor: Pergamon-Elsevier Science LTD
Oxford
Rights: fechado
Identifier DOI: 10.1016/j.eswa.2016.07.008
Address: http://www-sciencedirect-com.ez88.periodicos.capes.gov.br/science/article/pii/S0957417416303530?via%3Dihub
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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