Abstract
In brain magnetic resonance (MR) images, image quality is often degraded due to the influence of noise and outliers, which brings some difficulties for doctors to segment and extract brain tissue accurately. In this paper, a modified robust fuzzy c-means (MRFCM) algorithm for brain MR image segmentation is proposed. According to the gray level information of the pixels in the local neighborhood, the deviation values of each adjacent pixel are calculated in kernel space based on their median value, and the normalized adaptive weighted measure of each pixel is obtained. Both impulse noise and Gaussian noise in the image can be effectively suppressed, and the detail and edge information of the brain MR image can be better preserved. At the same time, the gray histogram is used to replace single pixel during the clustering process. The results of segmentation of MRFCM are compared with the state-of-the-art algorithms based on fuzzy clustering, and the proposed algorithm has the stronger anti-noise property, better robustness to various noises and higher segmentation accuracy.
Highlights
Magnetic resonance imaging (MRI) has been widely used in clinical diagnosis because of its advantages of non-ionizing radiation and wide applicability
To mitigate the effects of noise in brain magnetic resonance (MR) images, a fast image segmentation algorithm based on fuzzy c-means (FCM) clustering is proposed in this paper, it does not require a balance control factor, and the related parameters can be adaptively acquired from local neighborhood information
Compared with Enhanced FCM (EnFCM), fast generalized fuzzy c-means clustering (FGFCM) can improve the segmentation accuracy, and the parameter Sij can be changed with the change of local neighborhood window, which overcomes the defect of fixed parameter α to some extent and has better flexibility
Summary
Magnetic resonance imaging (MRI) has been widely used in clinical diagnosis because of its advantages of non-ionizing radiation and wide applicability. Ahmed et al [16] proposed an adaptively regularized kernel-based fuzzy c-means clustering (ARKFCM) algorithm for brain MR image segmentation. ARKFCM employed the heterogeneity of grayscales in the neighborhood and exploited a measure for local contextual information to replace the standard Euclidean distance with Gaussian radial basis kernel functions In these methods, some important details in the image may be lost due to the use of an image smoothing operation, especially boundary or edge. To mitigate the effects of noise in brain MR images, a fast image segmentation algorithm based on FCM clustering is proposed in this paper, it does not require a balance control factor, and the related parameters can be adaptively acquired from local neighborhood information.
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