Segmentation of thrombus in abdominal aortic aneurysms from CTA
with nonparametric statistical grey level appearance modeling
IEEE Transactions On Medical Imaging, 24 (4):
477-485 (апреля 2005)Аннотация
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 lis 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.
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