Geodesic Shooting for Computational Anatomy
Journal of Mathematical Imaging and Vision
Learning to Match: Deriving Optimal Template-Matching Algorithms from Probabilistic Image Models
International Journal of Computer Vision
A probabilistic approach for the simultaneous mammogram registration and abnormality detection
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Multi-stage learning for robust lung segmentation in challenging CT volumes
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
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Many techniques have been proposed to segment organs from images, however the segmentation of diseased organs remains challenging and frequently requires lots of user interaction. The challenge consists of segmenting an organ while its appearance and its shape vary due to the presence of the disease in addition to individual variations. We propose a template registration technique that can be used to recover the complete segmentation of a diseased organ from a partial segmentation. The usual template registration method is modified in such a way that it is robust to missing parts. The proposed method is used to segment Mycobacterium tuberculosis infected lungs in CT images of experimentally infected mice. Using synthetic data, we evaluate and compare the performance of the proposed algorithm with the usual sum of squared difference cost function.