Classification of medical images using edge-based features and sparse representation

M, Srinivas and C, Krishna Mohan (2016) Classification of medical images using edge-based features and sparse representation. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 20-25, March 2016, Shanghai.

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In this paper, an approach for classification of medical images using edge-based features is proposed. We demonstrate that the edge information extracted from an image by dividing the image into patches and each patch into concentric circular regions provide discriminative information useful for classification of medical images by considering 18 categories of radiological medical images namely, skull, hand, breast, cranium, hip, cervical spin, pelvis, radiocarpaljoint, elbow etc.,. The ability of On-line Dictionary Learning (ODL) to achieve sparse representation of an image is exploited to develop dictionaries for each class using edge-based feature. A low rate of misclassification error for these test images validates the effectiveness of edge-based features and On-line Dictionary Learning models for classification of medical images.

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Item Type: Conference or Workshop Item (Paper)
Additional Information: We would like to thank Dr. T.M. Deserno, Department of Medical Informatics, RWTH Aachen, Germany for making the original IRMA Database available for research purposes.
Subjects: Computer science > Special computer methods
Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 17 Jun 2016 09:07
Last Modified: 01 Sep 2017 09:14
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