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dc.contributorDepartment of Computing-
dc.creatorZuo, W-
dc.creatorWang, K-
dc.creatorZhang, DD-
dc.creatorZhang, H-
dc.publisherIEEE Computer Societyen_US
dc.rights© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectAutomated tongue segmentationen_US
dc.subjectActive contour modelen_US
dc.subjectPolar edge detectionen_US
dc.subjectTongue diagnosisen_US
dc.titleCombination of polar edge detection and active contour model for automated tongue segmentationen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: David Zhangen_US
dc.description.otherinformationBiometrics Research Centre, Department of Computingen_US
dcterms.abstractTongue diagnosis is an important diagnosis method in Traditional Chinese Medicine (TCM) and recently the development of automated tongue image analysis technology has been carried out. Automated tongue segmentation is difficult due to the complexity of pathological tongue, variance of tongue shape and interference of the lips. In this paper we present a novel method for automated tongue segmentation by combining polar edge detector and active contour model. First a novel polar edge detector is proposed to effectively extract the edge of the tongue body. We then introduce a method to filter out the edge that is useless for tongue segmentation. A local adaptive edge bi-thresholding technique is also proposed. Finally an initialization and active contour model are proposed to segment the tongue body from the image. Experimental results demonstrate that the novel tongue segmentation can segment the tongue accurately. A quantitative evaluation on 50 images indicates that the mean DCP (the distance to the closest point) of the proposed method is 5.86 pixels, and the average true positive (TP) percent is 97.2%.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the third International Conference on Image and Graphics : Hong Kong, China, 18-20 December 2004, p. 270-273-
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