| Summary: | 碩士 === 中山醫學大學 === 應用資訊科學學系碩士班 === 100 === The traditional detection of PE needs to rely on the professional judgments of physicians. With advances in CT technology both of the image quality and the number of images are improved, but it is also virtually increase the fatigue when physician in the diagnosis. Therefore, we propose a an pulmonary embolism detection system to reduce the fatigue when physician in the diagnosis.
First, we use a cubic contrast enhancement method to enhance branch vessels contrast in the pulmonary artery and lobar lung. It can highlight the PE in the branch vessels and help doctor to understand the PE degree. Next the texture and brightness feature in the image as the search characteristics of pulmonary embolism, in the part of the texture, we use Laws’ Mask to extract texture feature, and use Modification Average Distance to extract contrast feature. Finally we use the neural network to recognize pulmonary embolism with all features, extracted features. The experimental results showed that lung CT images after a cubic contrast enhancement method, the pulmonary blood vessels and branch vessels become clearly visible. Finally, we use 460 image obtained from 10 data sets to perfrom testing. We understand that the recognition rate of our proposed system is 98%.
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