A 3D level sets method for segmenting the mouse spleen and follicles in volumetric microCT images

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings

Volumn , Issue , 2006, Pages 2332-2336

  Price, Jeffery R ^a   Aykac, Deniz ^a   Wall, Jonathan ^b  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF TENNESSEE GRADUATE SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTERIZED TOMOGRAPHY; FINITE DIFFERENCE METHOD; IMAGE SEGMENTATION; IMAGE SENSORS; STATISTICAL METHODS;

INTERIOR FOLLICLES; MICROCT IMAGES; MOUSE SPLEEN;

LIVING SYSTEMS STUDIES;

ALGORITHM; ANIMAL; ANIMAL DISEASE; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; EVALUATION STUDY; IMAGE QUALITY; METHODOLOGY; MOUSE; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SPLEEN; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; IMAGING, THREE-DIMENSIONAL; MICE; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SPLEEN; TOMOGRAPHY, X-RAY COMPUTED;

EID: 34047094554     PISSN: 05891019     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2006.260127     Document Type: Conference Paper

References (12)

1. National Human Genome Research Institute. Background on mouse as a model organism. [Online]. Available: http://www.genome.gov/10005834
2. J. Wall, S. Kennel, M. Paulus, S. Gleason, J. Gregor, J. Baba, M. Schell, T. Richey, B. O'Nuallain, R. Donnell, P. Hawkins, D. Weiss, and A. Solomon, "Quantitative high-resolution microradiographic imaging of amyloid deposits in a novel murine model of AA-amyloidosis," Amyloid, vol. 12, no. 3, pp. 149-156, 2005.
3. J. Suri, K. Liu, S. Singh, S. Laxminarayan, X. Zeng, and L. Reden, "Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review," IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 1, pp. 8-28, 2002.
4. D. Aykac, J. Price, and J. Wall, "3D segmentation of the mouse spleen in microCT via active contours," in Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, vol. 3, 2005, pp. 1542-1545.
5. T. Chan and L. Vese, "Active contours without edges," IEEE Transactions on Image Processing, vol. 10, no. 2, pp. 266-277, 2001.
6. S. Osher and J. Sethian, "Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulation," Journal of Computational Physics, vol. 79, pp. 12-49, 1988.
7. L. Vese and T. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model," University of California, Los Angeles, UCLA CAM Report 01-25, 2001.
8. H.-K. Zhao, T. Chan, B. Merriman, and S. Osher, "A variational level set approach to multiphase motion," Journal of Computational Physics, vol. 127, pp. 179-195, 1996.
9. X. Han, C. Xu, and J. L. Prince, "A topology preserving level set method for geometric deformable models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 755-768, 2003.
10. B. Sumengen and B. Manjunath, "Graph partitioning active contours (GPAC) for image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 509-521, 2006.
11. J. Gregor, S. Gleason, M. Paulus, and J. Cates, "Fast Feldkamp reconstruction based on focus of attention and distributed computing," International Journal for Imaging Systems and Technology, vol. 12, no. 229-234, 2002.
12. A. Sarti, C. Corsi, E. Mazzini, and C. Lamberti, "Maximum likelihood segmentation of ultrasound images with rayleigh distribution," IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 52, no. 6, pp. 947-960, 2005.