TY - JOUR
T1 - Detection of small bowel tumor in wireless capsule endoscopy images using an adaptive neuro-fuzzy inference system
AU - Alizadeh, Mahdi
AU - Maghsoudi, Omid Haji
AU - Sharzehi, Kaveh
AU - Hemati, Hamid Reza
AU - Asl, Alireza Kamali
AU - Talebpour, Alireza
N1 - Publisher Copyright:
© 2017 by the Journal of Biomedical Research.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Adaptive neuro-fuzzy inference system
KW - Co-occurrence matrix
KW - Texture feature
KW - Wireless capsule endoscopy
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U2 - 10.7555/JBR.31.20160008
DO - 10.7555/JBR.31.20160008
M3 - Article
AN - SCOPUS:85029684680
SN - 1674-8301
VL - 31
SP - 419
EP - 427
JO - Journal of Biomedical Research
JF - Journal of Biomedical Research
IS - 5
ER -