Tongue Feature Analysis And Symptom Diagnosis Classification

Traditional Chinese Medicine (TCM) includes a range of traditional medical practices originated from China. Examination of the condition of the tongue is one of the most valuable and widely used diagnostic methods in TCM diagnostics and takes account of a wide variety of features including color, texture, and shape. In tongue diagnostics, changes in color, texture, and shape of tongue are sensitive indicators of internal pathological symptom and indispensable guideline for overall health state. In recent years, many researchers are dedicated to the combination of modern pattern recognition and image processing technologies, trying to find solutions to the non-quantitative issue of traditional tongue diagnosis. However, the further development of traditional tongue diagnosis is limited by its dependence on individual visual sensation and experience.In this dissertation of computerized tongue diagnosis, we investigate tongue feature analysis and diagnosis classification, including:(1) Feature Extraction of Tongue Color:Color is a visual perceptual property, and chromatic information of human tissue plays an important role for medical diagnosis. Tongue color is the most important characteristic for identifying diseases in tongue diagnostics. However, because of detailed visual discrimination based on the experience and knowledge of practitioners, there is much uncertainty and imprecision associated with tongue color in medicine. In order to eliminate these subjective factors, we have established a nonparametric semi-supervised (cluster and label) scheme to obtain labeled pixel samples. It applies both forward and backward selection procedures for constructing the pixel prototype sets with class labels. These pixel prototypes can classify all pixels of a tongue image into twelve classes of tongue colors.(2) Feature Extraction of Fungiform Papillae Hyperplasia (FPH):We propose a computer-aided system for identifying the presence or absence of Fungiform Papillae Hyperplasia (FPH). We first define and partition a region of interest (ROI) for texture acquisition. After preprocessing for detection and removal of reflective points, a set of 2D Gabor filter banks is used to extract and represent textural features. Then, we apply the Linear Discriminant Analysis (LDA) to identify the data sets from the tongue image database. The experimental results reasonably demonstrate the effectiveness of the method described in this paper.(3) Feature Extraction of Tongue Shape:We present a novel approach to the automated classification of the tongue shape. The first step is to permit accurate positioning for analysis by applying three geometric criteria to correct tongue deflection. Then we developed seven geometric features using various measures of length, area, and angle on the tongue. And seven modules of Analytic Hierarchy Process (AHP) are constructed to decipher the highly subjective and metaphorical human judgments as a well-defined and measurable machine representation. Each one is utilized to decide whether a tongue image belong to a specified class of the tongue shape. Finally, to allow the reliable machine-classification of tongue shapes, we applied a fuzzy fusion framework to formalize the uncertainty between the quantitative features and tongue shape classes. In experiments conducted on a total of 362 tongue samples, our system achieved an accuracy of 90.3%.(4) Classification of Eight Principal Symptom:Major research of diagnosis modeling is the classification model of "cold" and "hot" symptom based on the chromatic characteristic of "warm or cool". The terms "warm", "neutral", and "cool" are used to refer to the color of the tongue and are associated with various states of health. We propose a semi-supervised (cluster and label) scheme for classifying all pixels of a tongue image by color into three categories:"warm" "neutral", or "cool". The proposed scheme makes use of a classical clustering algorithm, Expectation Maximization, to divide all pixels into 150 clusters. Here, for all pixels in tongue color gamut,150 is a relatively large number and each cluster is on a small scale. Then manual labeling endows these clusters as category labels. Finally, we use a lookup table to classify all pixels into three categories of "warm or cool". Based on this result, we classify these images into "hot" or "cold" symptom. Except that, we utlize some features extracted from aforementionedsection, to assist the diagnosis of some symptoms. In this section, we list some classification results of "excess" and "deficiency" symptom, "exterior" and "interior" symptom, and these results are very helpful for the most import diagnostic method in TCM, "eight principal symptom diagnosis"In this dissertation, we carry out some investigations on tongue feature extraction and diagnosis modeling, which will be helpful for the computerized tongue diagnosis.