Abstract:

Tongues spots and petechiae are important tongue patterns in the computer tongue diagnosis system. We propose a method to recognize and extract spots and petechiae in tongue images based on blob detection, support vector machine (SVM) and k-means clustering. Firstly, we apply the SimpleBlobDetector algorithm to detect blobs in tongue images. Secondly, we obtain the characteristic values of blob number, size and distribution to generate the feature vector. Thirdly, we utilize the SVM classifier to recognize tongues with spots or petechiae. The detection of spots or petechiae also bases on blob detection. Blob detection result is clustered into several groups by using k-means clustering after extracting color features. To extract the spots or petechiae, we define a discriminant function based on weighted color space distance, compare the clustering results with the former blob detection results, and achieve a binary classification of clustering groups. The positive class is the extraction results. Experimental results show that the recognition accuracy can reach 97.4%, the false alarm rate is 6.0% and the missing alarm rate is 10.1%. The results also verify the availability and application value of our method.

Key words: tongue spots and petechiae, blob detection, feature extraction, support vector machine (SVM), K-means clustering