Classification of Medical Data Based On Sparse Representation Using Dictionary Learning

Mettu, Srinivas (2015) Classification of Medical Data Based On Sparse Representation Using Dictionary Learning. PhD thesis, Indian Institute of Technology Hyderabad.

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Abstract

Due to the increase in the sources of image acquisition and storage capacity, the search for relevant information in large medical image databases has become more challenging. Classification of medical data into different categories is an important task, and enables efficient cataloging and retrieval with large image collections. The medical image classification systems available today classify medical images based on modality, body part, disease or orientation. Recent work in this direction seek to use the semantics of medical data to achieve better classification. However, representation of semantics is a challenging task and sparse representation has been explored in this thesis for this task.

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Thesis (PhD)

Uncontrolled Keywords:

Content based medical image retrieval; classification; sparse representa- tion; dictionary learning; clustering; modality; multi-level classification; support vector machines; on-line dictionary learning; K-SVD; OMP; ℓ1-lasso; multi-scale dictionary learning; adaptive dictionary learning, TD325

Subjects:

Computer science > Special computer methods
Computer science > Big Data Analytics
Others > Information sciences

Divisions:

Department of Computer Science & Engineering

Depositing User:

Team Library

Date Deposited:

22 Jun 2015 06:30

Last Modified:

29 Jul 2019 10:54

URI:

http://raiithold.iith.ac.in/id/eprint/1586