C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

Miaojing Shi, Guo-Zheng Li, Fufeng Li

Research output: Contribution to journalArticlepeer-review

Abstract

Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C2G2FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C2G2FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C2G2FSnake realizes automatic tongue segmentation with greatly improved accuracy.
Original languageEnglish
Pages (from-to)1-14
Number of pages14
JournalScience China Information Sciences
Volume56
Issue number9
DOIs
Publication statusPublished - Sep 2013

Fingerprint

Dive into the research topics of 'C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge'. Together they form a unique fingerprint.

Cite this