Title: An automatic tongue detection and segmentation framework for computer-aided tongue image analysis

Authors: Ratchadaporn Kanawong; Wentao Xu; Dong Xu; Shao Li; Tao Ma; Ye Duan

Addresses: Computer Science Department and Informatics Institute, University of Missouri-Columbia, MO, 65211, USA ' Computer Science Department, East China Normal University, Shanghai, 200062, China ' Computer Science Department and Informatics Institute, University of Missouri-Columbia, MO, 65211, USA ' MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China ' MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, China ' Computer Science Department and Informatics Institute, University of Missouri-Columbia, MO, 65211, USA

Abstract: Traditional Chinese Medicine (TCM) has a long history and has been recognised as a popular alternative medicine in western countries. Tongue diagnosis is a significant procedure in computer-aided TCM, where tongue image analysis plays a dominant role. In this paper, we propose a fully automatic tongue detection and segmentation framework that includes a Principal Component Analysis (PCA)-based tongue detection algorithm as well as a new hybrid tongue segmentation algorithm that fully exploits the robustness of the Mean Shift algorithm and the efficiency of the Canny Edge Detection algorithm, together with the outlier removal capability of the Tensor Voting algorithm. Compared with other existing methods, our method is fully automatic without the need of adjusting parameters for different images and do not need any initialisation.

Keywords: tongue analysis; TCM; traditional Chinese medicine; tongue diagnosis; image segmentation; PCA; principal component analysis; mean shift; Canny edge detection; automatic tongue detection; computer-aided image analysis; tongue image analysis.

DOI: 10.1504/IJFIPM.2012.050420

International Journal of Functional Informatics and Personalised Medicine, 2012 Vol.4 No.1, pp.56 - 68

Received: 12 Sep 2011
Accepted: 02 Oct 2011
Published online: 20 Nov 2012 *

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