【作者】 罗煜；

【导师】 宋恩民；

【作者基本信息】 华中科技大学 ， 计算机软件与理论， 2007， 硕士

【摘要】 随着人们生活水平的提高,脂肪肝的发病率不断上升。临床上,医生使用B超仪诊断脂肪肝,仅凭肉眼进行定性和经验性的判断,诊断结果受主观因素影响较大,具有一定的局限性。利用计算机对肝脏B超图像进行纹理分析,获取量化参数,并以此为依据进行分类识别,有利于提高临床诊断的准确性和效率。基于B超图像的脂肪肝计算机辅助检测包括以下几个处理步骤:感兴趣区域选取、特征提取、分类识别、疾病程度量化分析。首先采用人机交互方式选择感兴趣区域。其次结合肝脏B超图像的特点,选取了近场回声细密度计算法、灰度共生矩阵法、局部灰度差分矩阵法、近远场灰度比计算法来提取脂肪肝和正常肝脏B超图像的纹理特征。根据显著性差异检验分析及各特征组合分类结果,确定了用于分类识别的最佳特征向量,包括近场回声细密度、灰度共生矩阵的角二阶矩、近远场灰度比。随后借助通用支持向量机软件包——LIBSVM设计了基于径向基核函数的支持向量机分类器。采用两种样本选择方案对分类器进行训练,利用训练好的分类器对正常肝和脂肪肝B超图像进行分类。最后通过计算待检测图像的特征向量与标准特征向量的相似性,实现对脂肪肝严重程度的判断。从武汉市第六医院提供的肝脏B超图像中选取有代表性的93幅图像进行实验,达到了对正常肝样本84%的识别率和脂肪肝97.1%的识别率,并实现了对典型轻、中度脂肪肝图像较好的分类效果。更多还原

【Abstract】 With the improvement of living conditions, fatty liver diseases have been increasing continuously. The main method to diagnose fatty liver is using B-scan ultrasound, which is often influenced by subjective factors and mainly depends on the experiences of doctors. Therefore, it will be helpful to enhance the accuracy and efficiency of clinical diagnosis by applying texture analysis theory into B-scan liver images to obtain the quantification feature characters and recognize these images.The specific steps of fatty liver computer-aided detection are as follows: selection of region of interest(ROI), feature extraction, classification recognition and disease severity quantification. In the first step, an interactive approach is proposed to select ROI. In the second step, combining with the characteristic of B-scan liver images, four methods of feature extraction have been chosen to differentiate normal and fatty liver images, including Near Field Echo Intensity(NFEI), Gray Level Co-occurrence Matrix(GLCM), Neighborhood Gray-Tone Difference Matrix(NGTDM) and Near Far Field Intensity Ratio(NFFIR). Making use of the statistical difference between normal and fatty liver group and the classification results of different feature combination, we finally set the best feature vector: NFEI, ASM of GLCM, NFFIR. In the part of classification recognition, the RBF kernel SVM classifier is designed by using the general SVM software development kits-LIBSVM. Two kinds of sample selection schemes have been adopted to train the classifier. With the well trained classifier, we classify the images into two groups, normal liver and fatty liver. At last, the similarity between the feature vector of a sample image and the standard feature vector is used to reflect the severity of a sample image. It achieves the goal of further analyzing the severity of fatty liver.The B-scan liver images are provided by the Sixth Hospital of Wuhan City. We choose 93 images from the dataset to carry out experiments. The classification rate for normal liver is 84% and for fatty liver is 97.1%. Good results on classifying mild and moderate fatty liver are also accomplished.更多还原