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Title: Application of texture features for classification of primary benign and primary malignant focal liver lesions
Authors: Manth N.
Virmani J.
Kumar V.
Kalra N.
Khandelwal N.
Published in: Studies in Computational Intelligence
Abstract: The present work focuses on the aspect of textural variations exhibited by primary benign and primarymalignant focal liver lesions. For capturing these textural variations of benign and malignant liver lesions, texture features are computed using statistical methods, signal processing based methods and transform domainmethods. As an application of texture description in medical domain, an efficient CAD system for primary benign i.e., hemangioma (HEM) and primary malignant i.e., hepatocellular carcinoma (HCC) liver lesions based on texture features derived from B-Mode liver ultrasound images of Focal liver lesions has been proposed in the present study. The texture features have been computed from the inside regions of interest (IROIs) i.e., from the regions inside the lesion and one surrounding region of interest (SROI) for each lesion. Texture descriptors are computed from IROIs and SROIs using six feature extraction methods namely, FOS, GLCM, GLRLM, FPS, Gabor and Laws’ features. Three texture feature vectors (TFVs) i.e., TFV1 consists of texture features computed from IROIs, TFV2 consists of texture ratio features (i.e., texture feature value computed from IROI divided by texture feature value computed from corresponding SROI) and TFV3 computed by combining TFV1 and TFV2 (IROIs texture features + texture ratio features) are subjected to classification by SVM and SSVM classifiers. It is observed that the performance of SSVM based CAD system is better than SVM based CAD system with respect to (a) overall classification accuracy (b) individual class accuracy for atypical HEM class and (c) computational efficiency. The promising results obtained from the proposed SSVM based CAD system design indicates its usefulness to assist radiologists for differential diagnosis between primary benign and primary malignant liver lesions. © Springer International Publishing Switzerland 2016.
Citation: Studies in Computational Intelligence (2016), 630(): 385-409
Issue Date: 2016
Publisher: Springer Verlag
Keywords: Computer aided diagnostic system
Focal liver lesions
Liver ultrasound images
Primary benign lesion
Primary malignant lesion
Smooth support vector machine classifier
Support vector machine classifier
Texture features
ISSN: 1860949X
Author Scopus IDs: 57148459300
Author Affiliations: Manth, N., Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Virmani, J., Thapar University-Patiala, Patiala, Punjab, India
Kumar, V., Indian Institute of Technology-Roorkee, Roorkee, India
Kalra, N., Post Graduate Institute of Medical Education and Research-Chandigarh, Chandigarh, India
Khandelwal, N., Post Graduate Institute of Medical Education and Research-Chandigarh, Chandigarh, India
Corresponding Author: Virmani, J.; Thapar University-PatialaIndia; email:
Appears in Collections:Journal Publications [EE]

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