Affiliations: [a] School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, Jiangsu, China
| [b] Imaging Department of the Changshu Affliated Hospital of Soochow University, Suzhou, Jiangsu, China
| [c] Intelligent Medical Technology Research Center of the Changshu Affliated Hospital of Soochow University, Suzhou, Jiangsu, China
Correspondence:
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Corresponding authors. Lijun Huang, Imaging Department of the Changshu Affliated Hospital of Soochow University, Suzhou, Jiangsu 215500, China. E-mail: [email protected]. and Kaijian Xia, Intelligent Medical Technology Research Center of the Changshu Affliated Hospital of Soochow University, Suzhou, Jiangsu 215500, China. E-mail: [email protected].
Abstract: In this paper, a novel semi-supervised fuzzy clustering algorithm, MFM-SFCM, based on a membership fusion mechanism is proposed for Diffusion-weighted imaging (DWI) brain infarction lesion segmentation. The proposed MFM-SFCM algorithm addresses the issue of weakened constraints and insufficient influence of labeled samples on the clustering process that arises in the semi-supervised fuzzy C-means clustering (SFCM) when emphasizing supervised information. By using a new membership fusion mechanism, MFM-SFCM eliminates this issue, greatly improving the accuracy of clustering results and accelerating convergence speed. This allows fuzzy clustering to achieve good results in the segmentation of DWI brain infarction lesions using a small amount of labeled information. The effectiveness of the MFM-SFCM algorithm is demonstrated through experiments conducted on a real-world dataset of DWI brain images.
