15 May 2003 Computerized detection of pulmonary embolism in 3D computed tomographic (CT) images: vessel tracking and segmentation techniques
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Abstract
We are developing a computer-aided diagnosis (CAD) system for detection of pulmonary embolism in computed tomographic (CT) images. An adaptive 3D pixel clustering method was developed based on Bayesian estimation and Expectation-Maximization (EM) analysis to segment vessels from their surrounding tissues. After a “connected component analysis”, the vessel tree was reconstructed by tracking the vessel and its branches in 3D space based on their geometric characteristics such as the tracked vessel direction and skeleton. The node location of splitting and merging of vessel branches were identified based on “bifurcation analysis”. 2D and 3D features of the tracked vessels and the surrounding tissues were used in a multi-dimensional feature space to identify PE from normal vessels. In this preliminary study, about 95% of the vessels could -be segmented and tracked even when the vessels were partially obstructed by PE ranging from 5% to 90%. Our method could detect 58% of all the PEs with an average 10.5 (ranging from 8 to 15) of false positives per case. 100% of the PEs could be detected if the average radius of vessels were larger than 2 mm and the vessels were partially obstructed by PE ranging from 20% to 80%.
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Chuan Zhou, Chuan Zhou, Lubomir M. Hadjiiski, Lubomir M. Hadjiiski, Berkman Sahiner, Berkman Sahiner, Heang-Ping Chan, Heang-Ping Chan, Smita Patel, Smita Patel, Philip Cascade, Philip Cascade, Ella A. Kazerooni, Ella A. Kazerooni, Jun Wei, Jun Wei, "Computerized detection of pulmonary embolism in 3D computed tomographic (CT) images: vessel tracking and segmentation techniques", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); doi: 10.1117/12.481369; https://doi.org/10.1117/12.481369
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