Atlas-based deformable mutual population segmentation
Abstract
Segmentation is one of the most critical problems in medical imaging. State-of-the art methods often are based on prior knowledge that can either encode geometry, appearance or both. Despite enormous work in the field, the mainstream is based on the concept of case-to-case segmentation. In this paper, we introduce the notion of mutual population segmentation using discrete optimization where results from a given example influence results for the rest of the examples towards improving the overall segmentation performance. The aim is to combine prior knowledge along with consistency through the simultaneous segmentation of the whole population. This is achieved through their mutual deformation towards the atlas, while being constrained through a simultaneous all-to-all deformable diffeomorphic registration. Promising results demonstrate the potentials of the method.
References
[1]
M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active contour models," IEEE ICCV, 1987.
[2]
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models - their training and application," CVIU, 1995.
[3]
T.F. Cootes, G.J. Edwards, and C.J. Taylor, "Active appearance models," IEEE T-PAMI, vol. 23, no. 6, pp. 681-685, 2001.
[4]
M. Rousson and N. Paragios, "Prior knowledge, level set representations and visual grouping," IJCV, 2008.
[5]
Y. Zhan, D. Shen, J. Zeng, L. Sun, G. Fichtinger, J. Moul, and C. Davatzikos, "Targeted Prostate Biopsy Using Statistical Image Analysis," IEEE T-MI, 2007.
[6]
L. Zollei, M. Shenton, W.M. Wells III, and K. Pohl, "The Impact of Atlas Formation Methods on Atlas-Guided Brain Segmentation," MICCAI, 2007.
[7]
L. Zollei, E. Learned-Miller, E. Grimson, and W. Wells, "Efficient population registration of 3d data," in IEEE ICCV, 2005.
[8]
K.K. Bhatia, J.V. Hajnal, B.K. Puri, A.D. Edwards, and D. Rueckert, "Consistent groupwise non-rigid registration for atlas construction," in IEEE ISBI, 2004.
[9]
A. Tikhonov, Ill-posed problems in natural sciences, Coronet, 1992.
[10]
D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes, "Nonrigid registration using free-form deformations: application to breast mr images," IEEE T-MI, vol. 18, no. 8, pp. 712-721, Aug. 1999.
[11]
D. Rueckert, P. Aljabar, R. Heckemann, J. Hajnal, and A. Hammers, "Diffeomorphic registration using b-splines," in MICCAI, 2006.
[12]
Y. Choi and S. Lee, "Injectivity conditions of 2d and 3d uniform cubic b-spline functions," Graphical Models, 2000.
[13]
B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N. Paragios, "Dense image registration through mrfs and efficient linear programming," MIA, 2008.
[14]
J. Shiraishi, S. Katsuragawa, J. Ikezoe, T. Matsumoto, T. Kobayashi, K. Komatsu, M. Matsui, H. Fujita, Y. Kodera, and K. Doi, "Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules," AJR, 2000.
[15]
B. van Ginneken, M.B. Stegmann, and M. Loog, "Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database," MIA, 2006.
[16]
N. Komodakis, G. Tziritas, and N. Paragios, "Fast, approximately optimal solutions for single and dynamic MRFs," in IEEE CVPR, 2007.
- Atlas-based deformable mutual population segmentation
Recommendations
Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures
Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image ...
LF-SegNet: A Fully Convolutional Encoder---Decoder Network for Segmenting Lung Fields from Chest Radiographs
Segmentation of lung fields is an important pre-requisite step in chest radiographic computer-aided diagnosis systems as it precisely defines the region-of-interest on which different operations are applied. However, it is immensely challenging due to ...
A Discrete MRF Framework for Integrated Multi-Atlas Registration and Segmentation
Multi-atlas segmentation has emerged in recent years as a simple yet powerful approach in medical image segmentation. It commonly comprises two steps: (1) a series of pairwise registrations that establish correspondences between a query image and a ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2009
1405 pages
ISBN:9781424439317
Publisher
IEEE Press
Publication History
Published: 28 June 2009
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 21 Dec 2024