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A diffusion‐based compensation approach for intensity inhomogeneity correction in MRI
Author(s) -
George Maryjo M.,
Kalaivani S.
Publication year - 2020
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22416
Subject(s) - computer science , intensity (physics) , compensation (psychology) , diffusion , parametric statistics , artifact (error) , fuzzy logic , algorithm , artificial intelligence , mathematics , physics , optics , statistics , psychology , psychoanalysis , thermodynamics
Intensity inhomogeneity is considered as an inherent artifact in magnetic resonance images and is prominent in high‐field strength scanners. An effective and conceptually simple retrospective correction technique is introduced in this article that implements a compensation function based on spatially constrained fuzzy c‐means clustering to reduce the effect of intensity inhomogeneity. Intensity compensation functions are estimated on each clustered region and are subsequently processed with an anisotropic diffusion strategy. The proposed approach does not require any parametric models or prior knowledge on the acquisition process for the intensity inhomogeneity correction. The proposed diffusion based technique was evaluated on simulated and real data sets and the results were compared with some of the prominent correction methods. The quantitative analyses in terms of coefficient of variation and coefficient of joint variation ensure the effectiveness of the proposed methodology. The experimental analyses of the results show that the proposed methodology outperforms the state‐of‐the‐art approaches.