A K-PLSR-based color correction method for TCM tongue images under different illumination conditions

In this paper a Kernel Partial Least Squares Regression (K-PLSR)-based color correction method for Traditional Chinese Medicine (TCM) tongue images under different illumination conditions has been proposed. The captured values under different illumination conditions and their reference values of 24 patches in the Munsell colorchecker are respectively considered as the input and the output. The mapping model between the input and the output is established by using the K-PLSR method in the device-independent CIE LAB color space. The mapping model is then applied to correct the captured tongue images. Experimental results show that, using the proposed method, the average color difference of each color patch is only 0.821 after correction. For the subjective results, tongue images under different illumination conditions can obtain consistent correction results, which is beneficial for subsequent standardized tongue image storage and automatic analysis in tongue diagnosis of TCM. Compared with the most commonly used polynomial-based correction method and support vector regression based correction method, whether for the subjective or objective evaluation, the proposed method can obtain a superior color correction performance.