access icon free Non-local-based spatially constrained hierarchical fuzzy C-means method for brain magnetic resonance imaging segmentation

Owing to the existence of noise and intensity inhomogeneity in brain magnetic resonance (MR) images, the existing segmentation algorithms are hard to find satisfied results. In this study, the authors propose an improved fuzzy C-mean clustering method (FCM) to obtain more accurate results. First, the authors modify the traditional regularisation smoothing term by using the non-local information to reduce the effect of the noise. Second, inspired by the mechanism of the Gaussian mixture model, the distance function of FCM is defined by using the form of certain exponential function consisting of not only the distance but also the covariance and the prior probability to improve the robustness. Meanwhile, the bias field is modelled by using orthogonal basis functions to reduce the effect of intensity inhomogeneity. Finally, they use the hierarchical strategy to construct a more flexibility function, which considers the improved distance function itself as a sub-FCM, to make the method more robust and accurate. Compared with the state-of-the-art methods, experiment results based on synthetic and real MR images demonstrate its accuracy and robustness.

Inspec keywords: medical image processing; mixture models; image denoising; Gaussian processes; biomedical MRI; probability; fuzzy set theory; image segmentation; pattern clustering

Other keywords: regularisation smoothing term; exponential function; Gaussian mixture model; intensity inhomogeneity effect reduction; orthogonal basis functions; FCM; noise effect reduction; flexibility function; hierarchical strategy; improved distance function; MR images; nonlocal-based spatially constrained hierarchical fuzzy C-means method; brain magnetic resonance imaging segmentation

Subjects: Medical magnetic resonance imaging and spectroscopy; Patient diagnostic methods and instrumentation; Other topics in statistics; Other topics in statistics; Biology and medical computing; Computer vision and image processing techniques; Algebra, set theory, and graph theory; Biomedical magnetic resonance imaging and spectroscopy; Optical, image and video signal processing; Combinatorial mathematics; Combinatorial mathematics

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