Title: Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system


Authors: Mahdi Alizadeh1, Omid Haji Maghsoudi1, Kaveh Sharzehi2, Hamid Reza Hemati3, Alireza Kamali Asl3, Alireza Talebpour4

Institutions: 1Department of Bioengineering, Temple University, Philadelphia, PA19121, USA; 2Department of Medicine, Section of Gastroenterology, School of Medicine, Temple University, Philadelphia, PA 19140, USA; 3Department of Radiation Medicine Engineering, and 4 Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran 1983963113, Iran.


Abstract: Automatic diagnosis tool helps physicians to evaluate capsule endoscopic examinations faster and more accurate. The purpose of this study was to evaluate the validity and reliability of an automatic post-processing method for identifying and classifying wireless capsule endoscopic images, and investigate statistical measures to differentiate normal and abnormal images. The proposed technique consists of two main stages, namely, feature extraction and classification. Primarily, 32 features incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence metrics were computed. Then, mutual information was used to select features with maximal dependence on the target class and with minimal redundancy between features. Finally, a trained classifier, adaptive neuro-fuzzy interface system was implemented to classify endoscopic images into tumor, healthy and unhealthy classes. Classification accuracy of 94.2% was obtained using the proposed pipeline. Such techniques are valuable for accurate detection characterization and interpretation of endoscopic images.


Keywords: adaptive neuro-fuzzy inference system, co-occurrence matrix, wireless capsule endoscopy, texture feature


Full Text:  JBR-2016-0008.pdf


J Biomed Res published on December 20th, 2016, doi:10.7555/JBR.31.20160008