TY - GEN
T1 - Registering histological and MR images of prostate for image-based cancer detection
AU - Zhan, Yiqiang
AU - Feldman, Michael
AU - Tomaszeweski, John
AU - Davatzikos, Christos
AU - Shen, Dinggang
PY - 2006/10/30
Y1 - 2006/10/30
N2 - This paper presents a deformable registration method to co-register histological images with MR images of the same prostate. By considering various distortion and cutting artifacts in histological images and also fundamentally different nature of histological and MR images, our registration method is thus guided by two types of landmark points that can be reliably detected in both histological and MR images, i.e., prostate boundary points, and internal salient points that can be identified by a scale-space analysis method. The similarity between these automatically detected landmarks in histological and MR images are defined by geometric features and normalized mutual information, respectively. By optimizing a function, which integrates the similarities between landmarks with spatial constraints, the correspondences between the landmarks as well as the deformable transformation between histological and MR images can be simultaneously obtained. The performance of our proposed registration algorithm has been evaluated by various designed experiments. This work is part of a larger effort to develop statistical atlases of prostate cancer using both imaging and histological information, and to use these atlases for optimal biopsy and therapy planning.
AB - This paper presents a deformable registration method to co-register histological images with MR images of the same prostate. By considering various distortion and cutting artifacts in histological images and also fundamentally different nature of histological and MR images, our registration method is thus guided by two types of landmark points that can be reliably detected in both histological and MR images, i.e., prostate boundary points, and internal salient points that can be identified by a scale-space analysis method. The similarity between these automatically detected landmarks in histological and MR images are defined by geometric features and normalized mutual information, respectively. By optimizing a function, which integrates the similarities between landmarks with spatial constraints, the correspondences between the landmarks as well as the deformable transformation between histological and MR images can be simultaneously obtained. The performance of our proposed registration algorithm has been evaluated by various designed experiments. This work is part of a larger effort to develop statistical atlases of prostate cancer using both imaging and histological information, and to use these atlases for optimal biopsy and therapy planning.
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M3 - Conference contribution
SN - 354044727X
SN - 9783540447276
VL - 4191 LNCS - II
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 620
EP - 628
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
Y2 - 1 October 2006 through 6 October 2006
ER -