An Assessment Method of Tongue Image Quality Based on Random Forest in Traditional Chinese Medicine

In the study and practice of the tongue characterization, experienced doctors found that a large number of the tongue images collected by tongue image instrument don’t meet the clinical requirement, which will directly affects the final result of tongue image analysis. In this paper, the automatic quality evaluation of tongue image is designed for the first time through the following steps. First, the original tongue images are processed. Second, statistics of local normalized luminance based on natural scene statistics (NSS) model, color, geometric and texture features of tongue images are extracted respectively. Finally, the Random Forest classifier is used to classify. Experimental results show that the method we proposed can get a better evaluation of tongue image quality. This approach can provide reliably reference data for assisted tongue image analysis.

[1]  Alan C. Bovik,et al.  No-reference quality assessment using natural scene statistics: JPEG2000 , 2005, IEEE Transactions on Image Processing.

[2]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[3]  D. Ruderman The statistics of natural images , 1994 .

[4]  Peiyi Shen,et al.  A license plate recognition system based on tamura texture in complex conditions , 2010, The 2010 IEEE International Conference on Information and Automation.

[5]  P. K. Sinha,et al.  Pruning of Random Forest classifiers: A survey and future directions , 2012, 2012 International Conference on Data Science & Engineering (ICDSE).

[6]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[7]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[8]  Shen Lan Image Analysis for Tongue Characterization , 2001 .

[9]  Alan C. Bovik,et al.  Blind/Referenceless Image Spatial Quality Evaluator , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[10]  Liu Feng,et al.  An efficient detection method for rare colored capsule based on RGB and HSV color space , 2014, 2014 IEEE International Conference on Granular Computing (GrC).