Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications.
The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a "connected component" analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries and veins, the segmentation accuracy are 97.0% (4546/4689) and 93.8% (4439/4732) for the cases without and with other lung diseases, respectively. Because of the lack of ground truth for the vessels, in addition to quantitative evaluation of the vessel segmentation performance, visual inspection was conducted to evaluate the segmentation. The results demonstrate that vessel segmentation using our method can extract the pulmonary vessels accurately and is not degraded by PE occlusion to the vessels in these test cases.
Zhou
Chuan
C
Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA. chuan@umich.edu
Chan
Heang-Ping
HP
Sahiner
Berkman
B
Hadjiiski
Lubomir M
LM
Chughtai
Aamer
A
Patel
Smita
S
Wei
Jun
J
Ge
Jun
J
Cascade
Philip N
PN
Kazerooni
Ella A
EA
D003936:Diagnosis, Computer-Assisted; D001330:Electronic Data Processing / Q000379:methods*; D005189:False Positive Reactions; D006801:Humans; D008954:Models, Biological; D010996:Pleural Effusion / Q000000981:diagnostic imaging; D011651:Pulmonary Artery / Q000000981:diagnostic imaging* / Q000503:physiopathology*; D011655:Pulmonary Embolism / Q000000981:diagnostic imaging*; D011667:Pulmonary Veins / Q000000981:diagnostic imaging / Q000503:physiopathology; D011856:Radiographic Image Enhancement / Q000379:methods*; D011871:Radiology; D014057:Tomography, X-Ray Computed