Tongue Shape Classification by Geometric Features

Abstract

Traditional Chinese Medicine diagnoses a wide range of health conditions by examining features of the tongue, including its shape. This chapter presents a classification approach for automatically recognizing and analyzing tongue shapes based on geometric features. The approach corrects tongue deflection by applying three geometric criteria and then classifies tongue shapes according to seven geometric features that are defined using various measurements of the length, area, and angle of the tongue. To establish a measurable and machine-readable relationship between expert human judgments and machine classifications of tongue shapes, we used a decision support tool, the analytic hierarchy process (AHP), to weigh the relative influences of the various length/area/angle factors used in classifying a tongue, and then applied a fuzzy fusion framework that combines seven AHP modules, one for each tongue shape, to represent the uncertainty and imprecision between these quantitative features and tongue shape classes. Experimental results show that the proposed shape correction method reduced the deflection of tongue shapes and that our shape classification approach, tested on 362 tongue samples, achieved an accuracy of 90.3%, making it more accurate than either KNN or LDA.