Registering histological and MR images of prostate for image-based cancer detection.
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.
Zhan
Yiqiang
Y
Sect. of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA. yzhang@cs.jhu.edu
Feldman
Michael
M
Tomaszeweski
John
J
Davatzikos
Christos
C
Shen
Dinggang
D
D000465:Algorithms; D001185:Artificial Intelligence; D001706:Biopsy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique