Feature-based registration of histopathology images with different stains: An application for computerized follicular lymphoma prognosis

Authors:
Lee Cooper;Olcay Sertel;Jun Kong;Gerard Lozanski;Kun Huang;Metin Gurcan
Affiliations:
Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 W. 10th Avenue, Columbus, OH 43210, United States;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 W. 10th Avenue, Columbus, OH 43210, United States;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 W. 10th Avenue, Columbus, OH 43210, United States;Department of Pathology, The Ohio State University Medical Center, 129 Hamilton Hall, 1645 Neil Avenue, Columbus, OH 43210, United States;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 W. 10th Avenue, Columbus, OH 43210, United States;Department of Biomedical Informatics, The Ohio State University, 3190 Graves Hall, 333 W. 10th Avenue, Columbus, OH 43210, United States
Venue:
Computer Methods and Programs in Biomedicine
Year:
2009

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Abstract

Follicular lymphoma (FL) is the second most common type of non-Hodgkin's lymphoma. Manual histological grading of FL is subject to remarkable inter- and intra-reader variations. A promising approach to grading is the development of a computer-assisted system that improves consistency and precision. Correlating information from adjacent slides with different stain types requires establishing spatial correspondences between the digitized section pair through a precise non-rigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods. This study proposes a method for the automatic non-rigid registration of histological section images with different stain types. This method is based on matching high level features that are representative of small anatomical structures. This choice of feature provides a rich matching environment, but also results in a high mismatch probability. Matching confidence is increased by establishing local groups of coherent features through geometric reasoning. The proposed method is validated on a set of FL images representing different disease stages. Statistical analysis demonstrates that given a proper feature set the accuracy of automatic registration is comparable to manual registration.