Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to the unit sphere, estimates the fuzzy boundaries location by thresholding the gradient magnitude of the rays lengths, and derives the true boundaries by Laplacian interpolation on the sphere. Its advantages are that it does not require a global shape prior or curvature based constraints, that it has an automatic stopping criteria, and that it is robust to anatomical variability, noise, and parameters values settings. Our experimental evaluation on 23 segmentations of kidneys and on 16 segmentations of abdominal aortic aneurysms (AAA) from CT scans yielded an average volume overlap error of 12.6% with respect to the ground-truth. These results are comparable to those of other segmentation methods without their underlying assumptions.
|Original language||American English|
|Title of host publication||Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings|
|Editors||Zhuowen Tu, Bjoern H. Menze, Nicholas Ayache, Hervé Delingette, Antonio Criminisi, Bjoern H. Menze, Le Lu, Georg Langs, Albert Montillo, Georg Langs, Polina Golland, Kensaku Mori|
|Number of pages||8|
|State||Published - 2012|
|Event||15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France|
Duration: 5 Oct 2012 → 5 Oct 2012
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012|
|Period||5/10/12 → 5/10/12|
Bibliographical notePublisher Copyright:
© Springer-Verlag Berlin Heidelberg 2012.