INTELLIGENT PULMONARY EMBOLISM DETECTION SYSTEM

Abstract

Pulmonary embolism (PE) is a blockage of the main artery of the lung or one of its branches by a substance that has travelled from elsewhere in the body through the bloodstream. Most of the traditional PE detection methods depend on the professional physician's judgment. Serious PE will lead to death. Therefore, diagnosis of PE is very important. In this paper, we develop an automatic PE detection system for relieving doctor's load. It is divided into five parts — preprocessing, finding pulmonary, vessel searching, vessel tracking and evaluation. In the finding pulmonary part, we use active contour model (ACM) to extract pulmonary area. Next, we use cubic curve contrast enhancement method to enhance the contrast of branch vessel image. Its objective is to highlight the embolism area in the branch vessel. And, in the main vessel searching, we employ k-means algorithm and Gaussian mixture model (GMM) to find the main vessel part. Moreover, we propose an effective vessel tracking method to achieve vessel tracking. Finally, we invite three radiologists to help us evaluate the performance of our proposed system and to obtain the ground truth image of our system. We can calculate the precision rate of our system according to these ground truth images. The experimental results show that a total of 62 false positives were obtained for the 16 cases. It means that the ratio of FP/DS is 3.875. At 3.875 FP/DS, the classification successful rate of our system is about 83% in main vessel detection and 82.6% precision rate in branch vessel detection. Finally, our system can relieve doctor's load according to the result of questionnaire analysis.