Quantitative Evaluation Of Liver Cancer Radiofrequency Treatment Based On Ultrasound Image Analysis

Liver cancer is one of the main causes of death. Data indicate that the number of people who has liver cancer is increasing. In recent years, radiofrequency ablation is widely used in the treatment of liver cancer because of its minimally invasive, safe, less impact on liver. Ultrasound images have the advantages of low-cost, real-time, no-radiation and repeatability, and become an important branch of medical imaging techniques. In clinical, ultrasound images can be used to evaluate the effects of radiofrequency ablation treatment for liver cancer, but its low-resolution and speckle noise are obstacle for doctors to observe and analyze the images. Therefore, many academics at home and abroad did a lot of research on ultrasound images by computer and have achieved certain results. But most studies just confirm or classify the liver disease by extracting the image feature and few people give a quantitative evaluation on the effect of radiofrequency treatment.In order to study the effect of liver cancer radiofrequency treatment, animal model experiments are performed at Dalian Medical University to get ultrasound images.The method of median filter is applied to ultrasound image preprocessing to suppress the speckle noise. In the experiment, gray co-occurrence matrix, gray level run length matrix, Gabor transform and wavelet decomposition methods are used to extract the texture features from different ultrasound images and the experiment results and analysis are given out.A quantitative evaluation method using variogram function and singular value decomposition is proposed in this paper. Texture features of the images are extracted from calculating the variogram function value, and then a quantitative evaluation system is obtained using the texture features to weight sum of the singular value. Test samples are used in this paper to test the reliability of the system, and the accuracy is 93.3%,so the quantitative evaluation system can be used to assist doctors to evaluating the effect of the liver cancer radiofrequency treatment more accurately.The region of interest of the ultrasound images we get are all manual segmentation. Image segmentation algorithm is studied in this paper and we hope that we can use automatic segmentation instead of manual segmentation to achieve automatic detection of cancer. Otsu threshold method, maximum entropy threshold method and fuzzy partition entropy method are used in ultrasound image segmentation and experiment results are given out.