Table of Contents
Journal of Medical Engineering
Volume 2014 (2014), Article ID 619081, 8 pages
Research Article

Quantitative Analysis of Diffusion Weighted MR Images of Brain Tumor Using Signal Intensity Gradient Technique

1Electronics and Communication Engineering, Gogte Institute of Technology, Belgaum 590008, India
2Indian Institute of Technology Madras, Belgaum 590009, India
3RAGAVS, Diagnostics and Research Center Pvt. Ltd., Bangalore 560011, India

Received 10 December 2013; Revised 14 April 2014; Accepted 12 May 2014; Published 28 May 2014

Academic Editor: Xiaofeng Jia

Copyright © 2014 S. S. Shanbhag et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The purpose of this study was to evaluate the role of diffusion weighted-magnetic resonance imaging (DW-MRI) in the examination and classification of brain tumors, namely, glioma and meningioma. Our hypothesis was that as signal intensity variations on diffusion weighted (DW) images depend on histology and cellularity of the tumor, analysing the signal intensity characteristics on DW images may allow differentiating between the tumor types. Towards this end the signal intensity variations on DW images of the entire tumor volume data of 20 subjects with glioma and 12 subjects with meningioma were investigated and quantified using signal intensity gradient (SIG) parameter. The relative increase in the SIG values (RSIG) for the subjects with glioma and meningioma was in the range of 10.08–28.36 times and 5.60–9.86 times, respectively, compared to their corresponding SIG values on the contralateral hemisphere. The RSIG values were significantly different between the subjects with glioma and meningioma , with no overlap between RSIG values across the two tumors. The results indicate that the quantitative changes in the RSIG values could be applied in the differential diagnosis of glioma and meningioma, and their adoption in clinical diagnosis and treatment could be helpful and informative.