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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/7160
Title: Characterization of primary and secondary malignant liver lesions from B-mode ultrasound
Authors: Virmani J.
Kumar V.
Kalra N.
Khandelwal N.
Published in: Journal of Digital Imaging
Abstract: Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm-support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6 % with sensitivity of 90 % and 93.3 % for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies. © 2013 Society for Imaging Informatics in Medicine.
Citation: Journal of Digital Imaging (2013), 26(6): 1058-1070
URI: https://doi.org/10.1007/s10278-013-9578-7
http://repository.iitr.ac.in/handle/123456789/7160
Issue Date: 2013
Keywords: Atypical metastasis
B-Mode liver ultrasound
Focal liver lesions
Genetic algorithm
Hepatocellular carcinoma
Large hepatocellular carcinoma
Metastasis
Primary malignant liver lesion
Secondary malignant liver lesion
Small hepatocellular carcinoma
Support vector machine classifier
Texture analysis
Typical metastasis
ISSN: 8971889
Author Scopus IDs: 54897388000
25646515800
7006315046
7006950739
Author Affiliations: Virmani, J., Biomedical Instrumentation Laboratory, Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
Kumar, V., Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India
Kalra, N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
Khandelwal, N., Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
Corresponding Author: Virmani, J.; Biomedical Instrumentation Laboratory, Department of Electrical Engineering, Indian Institute of Technology, Roorkee, Uttarakhand 247667, India; email: jitendra.virmani@gmail.com
Appears in Collections:Journal Publications [EE]

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