期刊
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
卷 6, 期 2, 页码 209-215出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11548-010-0506-8
关键词
Hyperspectral imaging; Vector rotation; Japanese traditional herbal medicine (Kampo Medicine); Medical diagnosis; Tongue image analysis
资金
- Joint Industry-Academia-Government
Purpose Kampo medicine (Japanese traditional herbal medicine) contains concepts useful for preventive medicine. For example, Mibyou (disease-oriented state) aims to prevent illness by early recognition. Kampo diagnosis is based on subjective examinations, such as tongue inspection, by trained specialist physicians. An objective metric of the tongue color spectrum was developed as a surrogate for subjective visual inspection. Methods Tongue images were acquired with a hyperspectral imaging system, and the uncoated tongue region was segmented automatically. The spectral information of the uncoated tongue area was analyzed by principal component analysis (PCA). The component vector most representative of each clinical symptom was found by rotating the vector on a plane spanned by two arbitrary principal component vectors. Results The system was tested in human volunteers. Forty-four hyperspectral images were acquired from 30 healthy male subjects for initial testing. The Oketsu (blood stagnation) score was determined by an experienced clinician in Kampo medicine from 27 of 30 subjects. The correlation between respective principal components and Oketsu score was 0.67 at maximum, and increased to 0.73 by linear combination, while it was -0.75 by vector rotation. Significant correlations for many disorders were demonstrated, and vector rotation showed better correlation than linear combination. Conclusions A PCA-based algorithm was developed to objectively evaluate patients using color images of the tongue surface. Testing showed that this method was a feasible surrogate for expert visual tongue analysis. This tool should help non-trained people identify Mibyou health status for individuals. The algorithm is free of empirical criteria, and it may be it applicable to many hyperspectral image types.
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