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 paper presents a classification approach for automatically recognizing and analyzing tongue shapes based on geometric features. The approach corrects the tongue deflection by applying three geometric criteria and then classifies tongue shapes according to seven geometric features defined using various measurements of length, area and angle of the tongue. To establish a measurable and machine readable relationship between expert human judgments and the machine classifications of tongue shapes, we use a decision support tool, Analytic Hierarchy Process (AHP), to weight the relative influences of the various length/area/angle factors used in classifying a tongue, and then apply 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 reduces the deflection of tongue shapes and that our shape classification approach, tested on a total of 362 tongue samples, achieved an accuracy of 90.3%, making it more accurate than either KNN or LDA.