Medical images modality classification using multi-scale dictionary learning

Mettu, Srinivas and C, Krishna Mohan (2014) Medical images modality classification using multi-scale dictionary learning. In: 19th International Conference on Digital Singal Processing, 20-23 August, 2014, Hong Kong.

Preview

Text
PID3252665.pdf - Accepted Version
Download (211kB) | Preview

Abstract

In this paper, we proposed a method for classification of medical images captured by different sensors (modalities) based on multi-scale wavelet representation using dictionary learning. Wavelet features extracted from an image provide discrimination useful for classification of medical images, namely, diffusion tensor imaging (DTI), magnetic resonance imaging (MRI), magnetic resonance angiography (MRA) and functional magnetic resonance imaging (FRMI). The ability of On-line dictionary learning (ODL) to achieve sparse representation of an image is exploited to develop dictionaries for each class using multi-scale representation (wavelets) feature. An experimental analysis performed on a set of images from the ICBM medical database demonstrates efficacy of the proposed method.

[error in script]

IITH Creators:

IITH Creators	ORCiD
C, Krishna Mohan	UNSPECIFIED

Item Type:

Conference or Workshop Item (Paper)

Additional Information:

Data collection and sharing for this project was provided by the International Consortium for Brain Mapping (ICBM; Principal Investigator: John Mazziotta, MD, PhD). ICBM funding was provided by the National Institute of Biomedical Imaging and BioEngineering. ICBM data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California.

Uncontrolled Keywords:

Multi-scale Dictionary Learning, Medical X-ray image, MRI, MRA, FMRA, DTI, Multi-scale representation, Sparse representation, ODL, Wavelet.

Subjects:

Computer science > Big Data Analytics

Divisions:

Department of Computer Science & Engineering

Depositing User:

Team Library

Date Deposited:

22 Jun 2015 07:30

Last Modified:

01 Sep 2017 09:25