Abstract: Liver cancer,Malignant liver tumors,can be divided into primary and secondary categories.Recent census data prove that the current annual mortality of liver cancer has ranked third in the world.The diagnosis of early liverdi-sease is beneficial to the treatment of liver cancer.The local binary pattern(LBP) algorithm has been widely used in the diagnosis of liver lesions.Although the traditional LBP method is simple,efficient,and easy to understand,but it lacks multi-scale information which leads to incomplete information description and lack of key information.In view of the defect that high order directional derivative local binary pattern(DLBP) algorithm will lose key information,extended multi-scale LBP algorithm(MSLBP) was proposed.The method firstly preprocesses the liver CT image to extract ROI region,then uses the extended multi-scale LBP feature extraction method to extract features.This method fuses the high-order sampling point information with its neighboring point information as the final information of the sampling point to participate in the operation.At the same time,the operation of averaging the diagonal regions highlights the neighborhood and describes the texture information of the liver image from a larger range.Finally,the classification algorithm is executed.The experimental results show that the accuracy of the proposed method can reach 90.1%,which is 8.7% higher than the original LBP feature extraction method.

Key words: Feature extraction, Local binary pattern, Medical image, Multi-scale, Texture analysis