Name
Abstract | ||
|---|---|---|
Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: "proximal", "middle", and "distal". The "proximal" and "distal" sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the "middle" partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy. and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds/slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the "proximal" and "distal" partitions. Complex methods are restricted to only the "middle" partition. The partition-enabled segmentation has been successfully tested and results are shown from multiple cases. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1117/12.533933 | Proceedings of SPIE |
Keywords | Field | DocType |
spatial relationships,algorithms,ground truth,rule based,volume rendering | Partition problem,Computer vision,Volume rendering,Scale-space segmentation,Computer science,Segmentation,Visualization,Contiguity (probability theory),Ground truth,Artificial intelligence,SIMPLE algorithm | Conference |
Volume | ISSN | Citations |
5370 | 0277-786X | 8 |
PageRank | References | Authors |
0.89 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srikanth Suryanarayanan | 1 | 12 | 3.93 |
| Rakesh Mullick | 2 | 71 | 14.86 |
| Yogish Mallya | 3 | 8 | 2.24 |
| Vidya Kamath | 4 | 8 | 0.89 |
| Nithin Nagaraj | 5 | 22 | 9.28 |