IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Aggregated Maximum Entropy Variational Analysis Method for Magnetic Resonance Imagery

Aggregated Maximum Entropy Variational Analysis Method for Magnetic Resonance Imagery
View Free PDF
Author(s): L. J. Morales-Mendoza (CINVESTAV of the IPN, Mexico), Y. V. Shkvarko (CINVESTAV of the IPN, Mexico), R. F. Vázquez-Bautista (CINVESTAV of the IPN, Mexico) and J. L. Ponce-Dávalos (CINVESTAV of the IPN, Mexico)
Copyright: 2004
Pages: 3
Source title: Innovations Through Information Technology
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-261-9.ch207
ISBN13: 9781616921255
EISBN13: 9781466665347

Abstract

There is a variety of computational paradigms for post-processing of biomedical magnetic resonance (MR) images based on the use of the maximum entropy (ME) image reconstruction method. Sometimes the ME-reconstructed image quality is insufficient to clinical analysis because of the degradations of many special features of the image, i.e., edges-stopping, localization of homogeneous zones, signal-to-noise ratios, textures degradation, etcetera. In this work, we propose to modify the conventional ME image reconstruction technique by aggregating it with the variational analysis method and address a new fused maximum-entropy-variational-analysis (MEVA) method for reconstruction and denoising of the MR images. Also, we propose an efficient computational scheme for numerical implementation of the MEVA algorithm using a Hopfield-type modified neural network, and demonstrate performance outcomes of the MEMA-reconstructed MR images.

Body Bottom