General Surface Energy for Spinal Cord and Aorta Segmentation

We present a new surface energy potential for the segmentation of cylindrical objects in 3D medical imaging using parametric spline active contours (a.k.a. spline-snakes). Our energy formulation is based on an optimal steerable surface detector. Thus, we combine the concept of steerability with spline-snakes that have open topology for semi-automatic segmentation. We show that the proposed energy yields segmentation results that are more robust to noise compared to classical gradient-based surface energies. We finally validate our model by segmenting the aorta on a cohort of 14 real 3D MRI images, and also provide an example of spinal cord segmentation using the same tool.


Published in:
Fourteenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'17)Proceedings of the, 319–322
Presented at:
Fourteenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'17), Melbourne, Commonwealth of Australia, April 18-21, 2017
Year:
2017
Publisher:
New York, IEEE
ISBN:
978-1-5090-1172-8
Keywords:
Laboratories:




 Record created 2017-05-04, last modified 2018-11-14

External links:
Download fulltextURL
Download fulltextURL
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)