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• W2896384138 abstract "Clustering based unsupervised segmentation approaches have become important tools to model data and infer knowledge. Such approaches are often used in various biomedical applications to assist experts in analyzing the data. Segmentation of brain magnetic resonance (MR) images has become a very challenging task due to the presence of Rician noise and intensity inhomogeneity. The existing methods dealing with such issues work in the spatial domain. These methods are prone to losing image details while reducing the effect of noise. The proposed method works in the moment domain, and for this purpose, we have selected Zernike moments (ZMs). The ZMs are orthogonal, rotation invariant and robust to image noise. The proposed method is based on our previous work on Zernike moments-based unbiased nonlocal means (ZM-UNLM) approach for denoising MR images affected by the Rician noise. The ZM-UNLM-based approach uses both the local and nonlocal information while eliminating Rician noise from brain MR images which have fine image details along with the noise components. Keeping in view the superior performance of the ZM-UNLM, we extend it to segment brain MR images and propose a method called local ZMs (LZM)-based unbiased nonlocal means fuzzy C-means (LZM-UNLM-FCM) approach which provides much-improved segmentation accuracy as compared to the existing spatial domain-based state-of-the-art techniques. It is shown how the LZMs based technique reduces the effect of the Rician noise while retaining the fine structures, edges, and other image details in the moment domain. The complete framework consists of an intelligent knowledge-driven approach that segments tissues from brain MR images using both the local and nonlocal information in the moment domain. The proposed approach has significance in developing future expert and intelligent systems that do not require any human intervention to manually annotate data and saves time for diagnostic related tasks. Detailed experimental results are provided to demonstrate the superior performance of the proposed method over the existing state-of-the-art methods." @default.
• W2896384138 created "2018-10-26" @default.
• W2896384138 creator A5031613595 @default.
• W2896384138 creator A5034904478 @default.
• W2896384138 date "2019-03-01" @default.
• W2896384138 modified "2023-10-18" @default.
• W2896384138 title "A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images" @default.
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• W2896384138 doi "https://doi.org/10.1016/j.eswa.2018.10.023" @default.
• W2896384138 hasPublicationYear "2019" @default.
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