【摘要】 计算机辅助诊断(Computer-Aided Diagnosis,CAD)系统为肺癌的早期检测和诊断提供了有力的支持.本文对孤立性肺结节特征提取问题进行研究.通过对肺结节和肺内各组织在序列CT图像上的医学征象分析和研究对比,结合专家提供的知识,提出了肺结节特征提取总体方案.该方案分别从肺部CT图像的灰度特征、肺结节形态、纹理、空间上下文特征等几个方面,对关键的医学征象进行图像分析,从而实现对ROI(Regions of Interest)区域的特征提取和量化;提出特征提取的评价方案,实验结果表明,本文提取的特征提取方案是有效的.利用本文提取的特征,肺结节检测正确率达到93.05%,敏感率为94.53%.更多还原

【Abstract】 Image processing techniques have proved to be effective for improvement of radiologists’ diagnosis of pubmonary nodules. In this paper,we present a strategy based on feature extraction technique aimed at Solitary Pulmonary Nodules (SPN) detection. In feature extraction scheme,36 features were obtained,contained 3 grey level features,16 morphological features,10 texture features and 7 spatial context features. And the classifier (SVM) running with the extracted features achieves comparative results,with a result of 93.05% in nodule detection accuracy and 94.53% in sensitivity.更多还原