A Graphical Model Approach To ATLAS-Free Mining Of MRI Images
Document Type
Conference Proceeding
Publication Date
2014
Published In
Proceedings Of The 2014 SIAM International Conference On Data Mining
Abstract
Improvements in medical imaging techniques have provided clinicians the ability to obtain detailed brain images of patients at lower costs. This increased availability of rich data opens up new avenues of research that promise better understanding of common brain ailments such as Alzheimer's Disease and dementia. Improved data mining techniques, however, are required to leverage these new data sets to identify intermediate disease states (e.g., mild cognitive impairment) and perform early diagnosis. We propose a graphical model framework based on conditional random fields (CRFs) to mine MRI brain images. As a proof-of-concept, we apply CRFs to the problem of brain tissue segmentation. Experimental results show robust and accurate performance on tissue segmentation comparable to other state-of-the-art segmentation methods. In addition, results show that our algorithm generalizes well across data sets and is less susceptible to outliers. Our method relies on minimal prior knowledge unlike atlas-based techniques, which assume images map to a normal template. Our results show that CRFs are a promising model for tissue segmentation, as well as other MRI data mining problems such as anatomical segmentation and disease diagnosis where atlas assumptions are unreliable in abnormal brain images.
Keywords
graphical models, image segmentation, brain images
Published By
SIAM
Editor(s)
M. Zaki, Z. Obradovic, P. N. Tan, A. Banerjee, C. Kamath, and S. Parthasarathy
Conference
2014 SIAM International Conference On Data Mining
Conference Dates
April 24-26, 2014
Conference Location
Philadelphia, PA
Recommended Citation
Christopher S. Magnano , '14; Ameet Soni; S. Natarajan; and G. Kunapuli.
(2014).
"A Graphical Model Approach To ATLAS-Free Mining Of MRI Images".
Proceedings Of The 2014 SIAM International Conference On Data Mining.
DOI: 10.1137/1.9781611973440.111
https://works.swarthmore.edu/fac-comp-sci/51