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Comparison of image intensity, local, and multi‐atlas priors in brain tissue classification
Author(s) -
Wang Liping,
Labrosse Frédéric,
Zwiggelaar Reyer
Publication year - 2017
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.12511
Subject(s) - prior probability , artificial intelligence , computer science , pattern recognition (psychology) , markov random field , preprocessor , segmentation , partial volume , image segmentation , computer vision , bayesian probability
Purpose Automated and accurate tissue classification in three‐dimensional brain magnetic resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity in homogeneity, and partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative study on the contributions of three commonly used image information priors for tissue classification in normal brains: image intensity, local, and multi‐atlas priors. Methods We compared the effectiveness of the three priors by comparing the four methods modeling them: K‐Means ( KM ), KM combined with a Markov Random Field ( KM ‐ MRF ), multi‐atlas segmentation ( MAS ), and the combination of KM , MRF , and MAS ( KM ‐ MRF ‐ MAS ). The key parameters and factors in each of the four methods are analyzed, and the performance of all the models is compared quantitatively and qualitatively on both simulated and real data. Results The KM ‐ MRF ‐ MAS model that combines the three image information priors performs best. Conclusions The image intensity prior is insufficient to generate reasonable results for a few images. Introducing local and multi‐atlas priors results in improved brain tissue classification. This study provides a general guide on what image information priors can be used for effective brain tissue classification.