Difference between revisions of "2013 Summer Project Week:Sobolev Segmenter"
| Line 51: | Line 51: | ||
==References== | ==References== | ||
| − | * A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to | + | * A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to IEEE Transactions on Image Processing. |
| − | IEEE Transactions on Image Processing. | ||
| − | * A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric | + | * A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808, Volume 21, Issue 7, pp. 3150-3156, July 2012. |
| − | Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808, | ||
| − | Volume 21, Issue 7, pp. 3150-3156, July 2012. | ||
Revision as of 18:03, 26 May 2013
Home < 2013 Summer Project Week:Sobolev Segmenter
The 2D segmentation of tumor by Sobolev active contour.
The 3D segmentation of tumor by Sobolev active contour.
Key Investigators
- UAB: Arie Nakhmani, LiangJia Zhu, Allen Tannenbaum
- BWH: Yi Gao, Ron Kikinis
- Utah: Rob MacLeod, Josh Cates
Objective
General segmentation tools can be used in a wide range of biomedical applications from tumor delineation to segmentation of the atrial wall used in the research on the atrial fibrillation problem. Sobolev active contour is a general 2D segmenter that can be used in the applications above. It can be extended for medical volume segmentation. Our objective is to implement Slicer's extension based on Sobolev active contours algorithm for volume segmentation.
Approach, Plan
We prepare C++ implementation of Sobolev active contour algorithm, and convert it to Slicer Commandline extension. This version needs a few sparse initial contours on some slices of the segmented volume.
In another approach, we implement similar algorithm in Python, as an Editor effect of Slicer. In this case, only the definition of 3D depth, and a single click inside the area of the segmented target are needed for the algorithm to work.
Progress
Alpha versions of both approaches (C++ Commandline and Python Editor effect) have been prepared.
Delivery Mechanism
This work will be delivered to the NA-MIC Kit as a loadable Commandline extension and an Editor effect.
References
- A. Nakhmani, A. Tannenbaum, "Tracking with Adaptive Sobolev Snakes." Submitted to IEEE Transactions on Image Processing.
- A. Nakhmani, A. Tannenbaum, "Self-Crossing Detection and Location for Parametric Active Contours," IEEE Transactions on Image Processing, DOI:10.1109/TIP.2012.2188808, Volume 21, Issue 7, pp. 3150-3156, July 2012.
