Mr. Zhengzhou Gao Profile

Zhengzhou Gao

at Beihang Univ

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Proceedings Article | 10 October 2020 Presentation + Paper

A bilateral-driven multi-centers clustering method for brain MRI segmentation

Zhengzhou Gao, Bo Liu, Xiaoyan Luo

Proceedings Volume 11550, 115500M (2020) https://doi.org/10.1117/12.2573979

KEYWORDS: Image segmentation, Magnetic resonance imaging, Brain, Neuroimaging, Tissues, Medical imaging, Medicine, Diagnostics, Image analysis

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Magnetic resonance imaging (MRI) is a common medical imaging technology in modern medicine. MRI images can provide valuable information for doctors to diagnose and treat patients, in which the segmentation of the brain into gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) is an important step for neural image analysis. Clustering methods are widely developed for this task. However, the traditional clustering segmentation method is easy to be affected by the initial clustering centers, which cause great trouble to accurately identify and extract the tissue. In this paper, we propose a bilateral-driven multi-centers clustering (BMC) method to segment brain MRI images, which integrates the pixel feature and spatial relationship. Firstly, the traditional fuzzy c-means (FCM) is employed to perform an initial rough segmentation. Secondly, we propose multi-centers seeking strategy based on the algorithm of clustering by fast search and find of density peaks (CFDP) to get primary multi-centers for each cluster. Thirdly, an iterative procedure is proposed to seek secondary centers and link all potential centers for each cluster, and in this process, the cluster labels of some pixels in the neighborhood of the secondary centers are determined accordingly. The proposed method is validated on the public simulated brain data from BrainWeb, experimental results show the proposed method can achieve a better segmentation performance than traditional methods. It is shown that our main strategy by multicenters can reasonably reflect the tissue distribution, which is advantageous than the traditional clustering segmentation method with an only single center.

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