A Comparative Study of Color Correction Algorithms for Tongue Image Inspection

Abstract

Color information is of great importance for the tongue inspection of computer-aided tongue diagnosis system. However, the RGB signals generated by different imaging device varied greatly due to dissimilar lighting conditions and usage of different kinds of digital cameras. This is a key problem for the tongue inspection and diagnosis. A common solution is to correct the tongue images to standard color space by the aid of colorchecker. In this paper, three general color correction techniques: polynomial regression, artificial neural network and support vector regression (SVR) are applied to the color correction of tongue image and compared for their performance of accuracy and time complexity. The experimental results of colorchecker correction show that when properly optimized, SVR performs the best among these three algorithms, with a training error of 0 and a test error of 0.68 to 3.03. The polynomial regression algorithm performs a little worse, but it is more robust to the fluctuations of the environmental illuminant and much faster than SVR to train the parameters. The ANN performs worst, and it is also time-consuming to train. Performance comparison to correct real tongue images shows that polynomial regression is better than SVR to achieve a close correction result to human perception. Finally, this paper is concluded that for tongue inspection in a computer-aided tongue diagnosis system, polynomial regression is suitable for online system correction to aid tongue diagnosis, while SVR technique offer a better alternative for the offline and automated tongue diagnosis.