Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model

Classification of magnetic resonance brain images using bi-dimensional empirical mode... Biomed Eng Lett (2015) 5:311-320 DOI 10.1007/s13534-015-0208-9 ORIGINAL ARTICLE Classification of Magnetic Resonance Brain Images using Bi-dimensional Empirical Mode Decomposition and Autoregressive Model Omkishor Sahu, Vijay Anand, Vivek Kanhangad and Ram Bilas Pachori Received: 25 July 2015 / Revised: 10 October 2015 / Accepted: 13 November 2015 © The Korean Society of Medical & Biological Engineering and Springer 2015 Abstract method for investigating brain abnormalities [1]. Images of Purpose Automated classification of brain magnetic resonance brain with some abnormalities are characterised by abrupt (MR) images has been an extensively researched topic in changes in image textures. For example, cancer in brain biomedical image processing. In this work, we propose a magnetic resonance image is characterised by large cells new approach for classifying normal and abnormal brain MR with high contrast [2], thus making it feasible to differentiate images using bi-dimensional empirical mode decomposition them from normal brain magnetic resonance images. (BEMD) and autoregressive (AR) model. Alzheimer's disease [3] is the common cause of age-related Methods In our approach, brain MR image is decomposed dementia. Multiple sclerosis [4] is a neurological disorder into four intrinsic mode functions (IMFs) using BEMD and that results in various dysfunctions. Other abnormalities AR coefficients http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model

Loading next page...
 
/lp/springer-journals/classification-of-magnetic-resonance-brain-images-using-bi-dimensional-y6wQk2ba4a

References (76)

Publisher
Springer Journals
Copyright
Copyright © 2015 by Korean Society of Medical and Biological Engineering and Springer
Subject
Engineering; Biomedical Engineering; Biophysics and Biological Physics; Biomedicine general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-015-0208-9
Publisher site
See Article on Publisher Site

Abstract

Biomed Eng Lett (2015) 5:311-320 DOI 10.1007/s13534-015-0208-9 ORIGINAL ARTICLE Classification of Magnetic Resonance Brain Images using Bi-dimensional Empirical Mode Decomposition and Autoregressive Model Omkishor Sahu, Vijay Anand, Vivek Kanhangad and Ram Bilas Pachori Received: 25 July 2015 / Revised: 10 October 2015 / Accepted: 13 November 2015 © The Korean Society of Medical & Biological Engineering and Springer 2015 Abstract method for investigating brain abnormalities [1]. Images of Purpose Automated classification of brain magnetic resonance brain with some abnormalities are characterised by abrupt (MR) images has been an extensively researched topic in changes in image textures. For example, cancer in brain biomedical image processing. In this work, we propose a magnetic resonance image is characterised by large cells new approach for classifying normal and abnormal brain MR with high contrast [2], thus making it feasible to differentiate images using bi-dimensional empirical mode decomposition them from normal brain magnetic resonance images. (BEMD) and autoregressive (AR) model. Alzheimer's disease [3] is the common cause of age-related Methods In our approach, brain MR image is decomposed dementia. Multiple sclerosis [4] is a neurological disorder into four intrinsic mode functions (IMFs) using BEMD and that results in various dysfunctions. Other abnormalities AR coefficients

Journal

Biomedical Engineering LettersSpringer Journals

Published: Jan 12, 2016

There are no references for this article.