Medical image segmentation based on SLIC superpixels model

Xiang-ting Chen; Fan Zhang; Ruo-ya Zhang

doi:10.1117/12.2258384

5 January 2017 Medical image segmentation based on SLIC superpixels model

Xiang-ting Chen, Fan Zhang, Ruo-ya Zhang

Proceedings Volume 10245, International Conference on Innovative Optical Health Science; 1024502 (2017) https://doi.org/10.1117/12.2258384
Event: International Conference on Innovative Optical Health Science, 2016, Shanghai Everbright International Hotel, China

Abstract

Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

Citation Download Citation

Xiang-ting Chen, Fan Zhang, and Ruo-ya Zhang "Medical image segmentation based on SLIC superpixels model", Proc. SPIE 10245, International Conference on Innovative Optical Health Science, 1024502 (5 January 2017); https://doi.org/10.1117/12.2258384

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