Table of Contents Author Guidelines Submit a Manuscript
Analytical Cellular Pathology
Volume 23, Issue 3-4, Pages 159-165

Automatic Registration and Error Detection of Multiple Slices Using Landmarks

Hans Frimmel,1 Lars Egevad,2 Christer Busch,3 and Ewert Bengtsson1

1Centre for Image Analysis, Uppsala, Sweden
2Department of Pathology, Karolinska Hospital, Stockholm, Sweden
3Department of Pathology, University Hospital, Tromsö, Norway

Copyright © 2001 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objectives. When analysing the 3D structure of tissue, serial sectioning and staining of the resulting slices is sometimes the preferred option. This leads to severe registration problems. In this paper, a method for automatic registration and error detection of slices using landmark needles has been developed. A cost function takes some parameters from the current state of the problem to be solved as input and gives a quality of the current solution as output. The cost function used in this paper, is based on a model of the slices and the landmark needles. The method has been used to register slices of prostates in order to create 3D computer models. Manual registration of the same prostates has been undertaken and compared with the results from the algorithm. Methods. Prostates from sixteen men who underwent radical prostatectomy were formalin fixed with landmark needles, sliced and the slices were computer reconstructed. The cost function takes rotation and translation for each prostate slice, as well as slope and offset for each landmark needle as input. The current quality of fit of the model, using the input parameters given, is returned. The function takes the built‐in instability of the model into account. The method uses a standard algorithm to optimize the prostate slice positions. To verify the result, s standard method in statistics was used. Results. The methods were evaluated for 16 prostates. When testing blindly, a physician could not determine whether the registration shown to him were created by the automated method described in this paper, or manually by an expert, except in one out of 16 cases. Visual inspection and analysis of the outlier confirmed that the input data had been deformed. The automatic detection of erroneous slices marked a few slices, including the outlier, as suspicious. Conclusions. The model based registration performs better than traditional simple slice‐wise registration. In the case of prostate slice registration, other aspects, such as the physical slicing method used, may be more important to the final result than the selection of registration method to use.