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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/6903
Title: A comparative study of computer-aided classification systems for focal hepatic lesions from B-mode ultrasound
Authors: Virmani J.
Kumar V.
Kalra N.
Khandelwal N.
Published in: Journal of Medical Engineering and Technology
Abstract: A comparative study of three computer-aided classification (CAC) systems for characterization of focal hepatic lesions (FHLs), such as cyst, hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET), along with normal (NOR) liver tissue is carried out in the present work. In order to develop efficient CAC systems a comprehensive and representative dataset consisting of B-mode ultrasound images with (1) typical and atypical cases of cyst, HEM and MET lesions, (2) small and large HCC lesions and (3) NOR liver cases have been used for designing K-nearest neighbour (KNN), probabilistic neural network (PNN) and a back propagation neural network (BPNN) classifiers. For differential diagnosis between atypical FHLs, expert radiologists often visualize the textural characteristics of regions inside and outside the lesion. Accordingly in the present work, texture features and texture ratio features are computed from regions inside and outside the lesions. A feature set consisting of 208 texture features (i.e. 104 texture features and 104 texture ratio features) is subjected to principal component analysis (PCA) for dimensionality reduction; it is observed that maximum accuracy of 87.7% is obtained for a PCA-BPNN-based CAC system in comparison to 86.1% and 85% as obtained by PCA-PNN and PCA-KNN-based CAC systems. The sensitivity of the proposed PCA-BPNN based CAC system for NOR, Cyst, HEM, HCC and MET cases is 82.5%, 96%, 93.3%, 90% and 82.2%, respectively. The sensitivity values with respect to typical, atypical, small HCC and large HCC cases are 85.9%, 88.1%, 100% and 87%, respectively. Keeping in view the comprehensive and representative dataset used for designing the classifier, the results obtained by the proposed PCA-BPNN-based CAC system are quite encouraging and indicate its usefulness to assist experienced radiologists for interpretation and diagnosis of FHLs. © 2013 Informa UK Ltd.
Citation: Journal of Medical Engineering and Technology (2013), 37(4): 292-306
URI: https://doi.org/10.3109/03091902.2013.794869
http://repository.iitr.ac.in/handle/123456789/6903
Issue Date: 2013
Keywords: B-mode ultrasound
Computer-aided classification system
Focal hepatic lesions
K-nearest neighbour
Neural network
Principal component analysis
Probabilistic neural network
Texture analysis
ISSN: 3091902
Author Scopus IDs: 54897388000
25646515800
7006315046
7006950739
Author Affiliations: Virmani, J., Indian Institute of Technology - Roorkee, Roorkee, Uttarakhand, 247667, India
Kumar, V., Indian Institute of Technology - Roorkee, Roorkee, Uttarakhand, 247667, India
Kalra, N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India
Khandelwal, N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, 160012, India
Corresponding Author: Virmani, J.; Indian Institute of Technology - Roorkee, Roorkee, Uttarakhand, 247667, India; email: jitendra.virmani@gmail.com
Appears in Collections:Journal Publications [EE]

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