A modified gaussian mixture model algorithm for image segmentation based on dependable spatial constraints

作者：Cai Weiling^*; Lei Lei

来源：International Journal of Digital Content Technology and Its Applications, 2012, 6(2): 255-268.

DOI：10.4156/jdcta.vol6.issue2.31

• Clustering analysis
• Gaussian Mixture Model
• High reliability
• Medical images
• Segmentation accuracy
• Segmentation performance
• Segmentation process
• Spatial constraints
• Spatial informations

In this paper, a modified Gaussian Mixture Model algorithm based on dependable spatial constraints is proposed for image segmentation. In order to enhance the segmentation performance, the proposed algorithm utilizes the consistence between the pixel and its local window to discriminate uncorrupted pixels from corrupted pixels. Then, using these uncorrupted pixels, the dependable spatial constraints are applied to influence the labeling of the pixel. In this way, the spatial information with high reliability is incorporated into the segmentation process, as a result, the segmentation accuracy is guaranteed to a great extent. The extensive segmentation experiments on synthetic, real and medical images demonstrate the effectiveness of the proposed algorithm.

• 出版日期2012
• 单位南京师范大学