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Information 2019, 10(2), 74; https://doi.org/10.3390/info10020074

A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation

1
School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China
2
School of Electronic Engineering, Heilongjiang University, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Received: 19 January 2019 / Revised: 13 February 2019 / Accepted: 19 February 2019 / Published: 21 February 2019
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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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. View Full-Text
Keywords: fuzzy c-means clustering; image segmentation; spatial constraints; brain magnetic resonance image fuzzy c-means clustering; image segmentation; spatial constraints; brain magnetic resonance image
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Song, J.; Zhang, Z. A Modified Robust FCM Model with Spatial Constraints for Brain MR Image Segmentation. Information 2019, 10, 74.

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