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A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis
Joint Authors
Ooi, Chia Yee
Odaguchi, Hiroshi
Kamarudin, Nur Diyana
Kawanabe, Tadaaki
Kobayashi, Fuminori
Source
Journal of Healthcare Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-04-20
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In tongue diagnosis, colour information of tongue body has kept valuable information regarding the state of disease and its correlation with the internal organs.
Qualitatively, practitioners may have difficulty in their judgement due to the instable lighting condition and naked eye’s ability to capture the exact colour distribution on the tongue especially the tongue with multicolour substance.
To overcome this ambiguity, this paper presents a two-stage tongue’s multicolour classification based on a support vector machine (SVM) whose support vectors are reduced by our proposed k-means clustering identifiers and red colour range for precise tongue colour diagnosis.
In the first stage, k-means clustering is used to cluster a tongue image into four clusters of image background (black), deep red region, red/light red region, and transitional region.
In the second-stage classification, red/light red tongue images are further classified into red tongue or light red tongue based on the red colour range derived in our work.
Overall, true rate classification accuracy of the proposed two-stage classification to diagnose red, light red, and deep red tongue colours is 94%.
The number of support vectors in SVM is improved by 41.2%, and the execution time for one image is recorded as 48 seconds.
American Psychological Association (APA)
Kamarudin, Nur Diyana& Ooi, Chia Yee& Kawanabe, Tadaaki& Odaguchi, Hiroshi& Kobayashi, Fuminori. 2017. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis. Journal of Healthcare Engineering،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1181178
Modern Language Association (MLA)
Kamarudin, Nur Diyana…[et al.]. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis. Journal of Healthcare Engineering No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1181178
American Medical Association (AMA)
Kamarudin, Nur Diyana& Ooi, Chia Yee& Kawanabe, Tadaaki& Odaguchi, Hiroshi& Kobayashi, Fuminori. A Fast SVM-Based Tongue’s Colour Classification Aided by k-Means Clustering Identifiers and Colour Attributes as Computer-Assisted Tool for Tongue Diagnosis. Journal of Healthcare Engineering. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1181178
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1181178