Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling.
This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.
Olabarriaga
Silvia D
SD
University Medical Center Utrecht, Image Sciences Institute, Q.S. 459, Heidelberglaan 100, 3584CX Utrecht, The Netherlands. silvia@isi.uu.nl
Rouet
Jean-Michel
JM
Fradkin
Maxim
M
Breeuwer
Marcel
M
Niessen
Wiro J
WJ
D000465:Algorithms; D000792:Angiography / Q000379:methods; D017544:Aortic Aneurysm, Abdominal / Q000150:complications / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003196:Computer Graphics; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013927:Thrombosis / Q000000981:diagnostic imaging* / Q000209:etiology; D014057:Tomography, X-Ray Computed / Q000379:methods