Study On Key Technologies Of Differentiating Liver Cancer From Normal Liver Form Ultrasound Image Texture Features

Liver cancer is the main disease harming human heath and even imperiling human life, so that it is early accuracily diagnosed is urgent affairs. with the fast development of computer and image processing techniques, medical images have become the major information resources of liver disease dignosis and meanwhile it promotes the advance of model methods extracting image(including medical imagery) texture features. It will better make up the defect that the method of visually detecting liver ultrasound image does not far meet the requirements of accurate diagnosis. therefore, the research has become the hot issue.This dissertion deeply studied the classifation and recognition of liver ultrasonic images by means of computer-aided diagnosis techiniqus, taking normal liver and liver cancer ultrasonic images for samples and taking the fractal features of liver ultrasonic image texture for information. The research contents include:(1)Statistic analysis to fractal dimension lacunarity. the dissertion by comtrast analyzed4fractal dimension methods of good performance behaving in other fields about image texture feature exreaction-blanket, Fourier power spectrum(FPS), fractional Brownian motion(FBM), differential box counting(DBC). Meanwhile, it also analyzed another parameter in fractal geometry-lacunarity,5of its estimating methods-Mandelbrot, lacunarity of differential box counting(LDBC), lacunarity of box column range(LBCR), lacunarity of cube box mass(LCBM) and lacunarity of box column mean(LBCM). Small sample normal distribution goodness of fit test and small sample double sided Student-t test indicate that the fractal dimension values got by4methods all basically presented normal distributions, and the fractal dimension mean values of normal liver and liver cancer ultrasonic images attained with4fractal dimension methods all presented significant difference at0.05confidence level. Except for LBCR, the lacunarity values at best scale achieved with other lacunarity methods all indicated normal distributions, and yet the lacunarity mean values of normal liver and liver cancer ultrasonic image achieved with only LCBM and the proposed LBCM in this dissertion presented significant difference at0.05confidence level. Except FBM, the fractal dimension mean values of normal liver ultrasonic image all were less than those of liver cancer ultrasonic image attained with other fractal dimension methods, and likewise the lacunarity mean values of normal liver ultrasonic images were less than that of liver cancer ultrasonic images attained with LCBM and LBCM lacunarity methods.(2)Classification to fractal dimension and lacunarity with SVM and ROC. in order to verify the abilities of describing liver ultrasonic image texture features by4fractal dimension and5lacunarity methods related above, hereon one evaluated and classified the fractal dimension and lacunarity values of normal liver and liver cancer ultrasonic images achieved by above fractal dimension and lacunarity methods with Receiver Operating Characteristic(ROC) and Support Vector Machine(SVM). Evaluation and classification show that FPS of fractal dimension methods and LBCM of lacunarity methods achieved higher area under receiver operating characteristic curves(AUC) and higher classification accuracy.(3)The combined factors of fractal dimension and lacunarity with SVM and ROC. taking4fractal dimension values and LBCM lacunarity value for single factors and taking the combination of4fractal dimension factors with LBCM lacunarity respectively for the combined factors(blanket+LBCM、FPS+LBCM、FBM+LBCM、DBC+LBCM), one, by contrast, analyzed and evaluated the single factors and the combined factors with ROC and SVM. Evaluation and classification results indicate that except the single factors, FPS and LBCM, remain higher AUC and classification accuracy, the combined factors, FPS+LBCM and DBC+LBCM, hold higher AUC and classification accuracy than their corresponding single factors and other combined factors.(4)Contrastive analysis of3multifractals with ROC. the samples for normal liver and liver cancer ultrasound image were analyzed with multifractal method by taking box-sum, wavelet coefficient and wavelet leader for the measures. Experimental results indicate that the measure for box-sum attains maximal AUC value in the range of [-1,1] and hold the more strong ability catching liver ultrasound image texture feature as well as liver ultrasound image texture feature is fractal features of low dimension.31figures,23tables,127refences...