Tsallis-Entropy Segmentation through MRF and Alzheimer anatomic reference for Brain Magnetic Resonance Parcellation

Author(s)

Azimbagirad, Mehran ^{[1, 2]} ; Simozo, Fabricio H. ^[1] ; Senra Filho, Antonio C. S. ^[1] ; Murta Junior, Luiz O. ^[1]

Affiliation

^[1] Univ Sao Paulo, FFCLRP, Dept Comp & Math, Ribeirao Preto, SP - Brazil

^[2] Univ Sao Paulo, FFCLRP, Dept Phys, Ribeirao Preto, SP - Brazil

• Document type: Journal article
• Source: MAGNETIC RESONANCE IMAGING; v. 65, p. 136-145, JAN 2020.
• Web of Science Citations: 0

Abstract

Quantifying the intracranial tissue volume changes in magnetic resonance imaging (MRI) assists specialists to analyze the effects of natural or pathological changes. Since these changes can be subtle, the accuracy of the automatic compartmentalization method is always criticized by specialists. We propose and then evaluate an automatic segmentation method based on modified q-entropy (Mqe) through a modified Markov Random Field (MMRF) enhanced by Alzheimer anatomic reference (AAR) to provide a high accuracy brain tissues parcellation approach (Mqe-MMRF). We underwent two strategies to evaluate Mqe-MMRF; a simulation of different levels of noise and non-uniformity effect on MRI data (7 subjects) and a set of twenty MRI data available from MRBrainS13 as patient brain tissue segmentation challenge. We accessed eleven quality metrics compared to reference tissues delineations to evaluate Mqe-MMRF. MRI segmentation scores decreased by only 4.6% on quality metrics after noise and non-uniformity simulations of 40% and 9%, respectively. We found significant mean improvements in the metrics of the five training subjects, for whole-brain 0.86%, White Matter 3.20%, Gray Matter 3.99%, and Cerebrospinal Fluid 4.16% (p-values < 0.02) when Mqe-MMRF compared to the other reference methods. We also processed the Mqe-MMRF on 15 evaluation subjects group from MRBrainS13 online challenge, and the results held a higher rank than the reference tools; FreeSurfer, SPM, and FSL. Since the proposed method improved the precision of brain segmentation, specifically, for GM, and thus one can use it in quantitative and morphological brain studies. (AU)

• FAPESP's process: 17/20598-9 - Computational methods for surgeries and therapies planning, evaluation, and follow-up through medical images.
• Grantee: Luiz Otavio Murta Junior
• Support Opportunities: Regular Research Grants