关键词: 肝癌超声图像, 纹理分析, 分维, 孔隙度, 协同作用, 支持向量机

Abstract: The performance of describing ultrasonic liver cancer image texture feature with fractal and lacunarity and their combined factors is studied. The fractal dimension and lacunarity values are estimated with 4 fractal dimension and a lacunarity methods based on the samples of 14 ultrasonic images each for normal liver and liver cancer. ROC（Receiver Operating Characteristic） analysis shows that the single factors for FPS and LBCM hold higher AUCs（area under ROC curve）. The train and test results for the single factors and the combined factors with SVM（Support Vector Machine）exhibit that FPS（Fourier Power Spectrum）+LBCM（Lacunarity of Box Column Mean）（4 kernals） and DBC（Differential Box Counting）+LBCM（except SIGMOID） get higher classification accuracy rate than the single factors.

Key words: ultrasonic liver cancer image, texture analysis, fractal dimension, lacunarity, synergy, Support Vector Machine（SVM）