By Topic

Application of Radial Ray Based Segmentation to Cervical Lymph Nodes in CT Images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Steger, S. ; Fraunhofer IGD, Darmstadt, Germany ; Bozoglu, N. ; Kuijper, A. ; Wesarg, S.

The 3D-segmentation of lymph nodes in computed tomography images is required for staging and disease progression monitoring. Major challenges are shape and size variance, as well as low contrast, image noise, and pathologies. In this paper, radial ray based segmentation is applied to lymph nodes. From a seed point, rays are cast into all directions and an optimization technique determines a radius for each ray based on image appearance and shape knowledge. Lymph node specific appearance cost functions are introduced and their optimal parameters are determined. For the first time, the resulting segmentation accuracy of different appearance cost functions and optimization strategies is compared. Further contributions are extensions to reduce the dependency on the seed point, to support a larger variety of shapes, and to enable interaction. The best results are obtained using graph-cut on a combination of the direction weighted image gradient and accumulated intensities outside a predefined intensity range. Evaluation on 100 lymph nodes shows that with an average symmetric surface distance of 0.41 mm the segmentation accuracy is close to manual segmentation and outperforms existing radial ray and model based methods. The method's inter-observer-variability of 5.9% for volume assessment is lower than the 15.9% obtained using manual segmentation.

Published in:

Medical Imaging, IEEE Transactions on  (Volume:32 ,  Issue: 5 )