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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/7863
Title: SVM-Based CAC System for B-Mode Kidney Ultrasound Images
Authors: Subramanya M.B.
Kumar V.
Mukherjee S.
Saini M.
Published in: Journal of Digital Imaging
Abstract: The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases. © 2014, Society for Imaging Informatics in Medicine.
Citation: Journal of Digital Imaging (2015), 28(4): 448-458
URI: https://doi.org/10.1007/s10278-014-9754-4
http://repository.iitr.ac.in/handle/123456789/7863
Issue Date: 2015
Publisher: Springer New York LLC
Keywords: Classification
Feature selection
Support vector machine
Texture features
Ultrasound kidney images
ISSN: 8971889
Author Scopus IDs: 56088524300
25646515800
57212613939
7006223234
Author Affiliations: Subramanya, M.B., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Kumar, V., Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand 247667, India
Mukherjee, S., Moradabad Institute of Technology, Moradabad, Uttar Pradesh 244001, India
Saini, M., Department of Radiology, Himalayan Institute of Medical Sciences, HIHT University, PO Doiwala, Dehradun, 248140, India
Corresponding Author: Subramanya, M.B.; Department of Electrical Engineering, Indian Institute of Technology RoorkeeIndia
Appears in Collections:Journal Publications [EE]

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