TY - GEN
T1 - Fast 3D brain segmentation using dual-front active contours with optional user-interaction
AU - Li, Hua
AU - Yezzi, Anthony
AU - Cohen, Laurent D.
PY - 2005
Y1 - 2005
N2 - Important attributes of 3D brain segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours, which minimize image-based energies in a manner that yields more global minimizers compared to standard active contours. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the global nature of the evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.
AB - Important attributes of 3D brain segmentation algorithms include robustness, accuracy, computational efficiency, and facilitation of user interaction, yet few algorithms incorporate all of these traits. Manual segmentation is highly accurate but tedious and laborious. Most automatic techniques, while less demanding on the user, are much less accurate. It would be useful to employ a fast automatic segmentation procedure to do most of the work but still allow an expert user to interactively guide the segmentation to ensure an accurate final result. We propose a novel 3D brain cortex segmentation procedure utilizing dual-front active contours, which minimize image-based energies in a manner that yields more global minimizers compared to standard active contours. The resulting scheme is not only more robust but much faster and allows the user to guide the final segmentation through simple mouse clicks which add extra seed points. Due to the global nature of the evolution model, single mouse clicks yield corrections to the segmentation that extend far beyond their initial locations, thus minimizing the user effort. Results on 15 simulated and 20 real 3D brain images demonstrate the robustness, accuracy, and speed of our scheme compared with other methods.
UR - http://www.scopus.com/inward/record.url?scp=33646682589&partnerID=8YFLogxK
U2 - 10.1007/11569541_34
DO - 10.1007/11569541_34
M3 - Conference contribution
AN - SCOPUS:33646682589
SN - 3540294112
SN - 9783540294115
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 335
EP - 345
BT - Computer Vision for Biomedical Image Applications - First International Workshop, CVBIA 2005, Proceedings
PB - Springer Verlag
T2 - 1st International Workshop on Computer Vision for Biomedical Image Applications, CVBIA 2005
Y2 - 21 October 2005 through 21 October 2005
ER -