Registering Histological and MR Images of Prostate for Image-Based Cancer Detection

  • Yiqiang Zhan
  • Michael Feldman
  • John Tomaszeweski
  • Christos Davatzikos
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


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.


Mutual Information Cancerous Region Registration Method Normalize Mutual Information Histological Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yiqiang Zhan
    • 1
    • 3
  • Michael Feldman
    • 2
  • John Tomaszeweski
    • 2
  • Christos Davatzikos
    • 1
  • Dinggang Shen
    • 1
  1. 1.Sect. of Biomedical Image AnalysisUniversity of PennsylvaniaPhiladelphia
  2. 2.Dept. of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphia
  3. 3.Dept. of Computer ScienceJohns Hopkins UniversityBaltimore

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