CIALAB Follicular Lymphoma research to be published in the Computer Methods and Programs in Biomedicine Journal
The article entitled "Feature-Based Registration of Histopathology Images with Different Stains: An Application for Computerized Follicular Lymphoma Prognosis" has been accepted for publication in the Computer Methods and Programs in Biomedicine Journal. This study is a part of the computer-aided system being developed for the automated histopathological evaluation of Follicular Lymphoma, one of the most common type of lymphoid malignancy affecting more than fifty thousand people in the United States annually. The abstract of this study appears below:
Feature-Based Registration of Histopathology Images with Different Stains: An Application for Computerized Follicular Lymphoma Prognosis
Follicular lymphoma (FL) is the second most common type of non-Hodgkin’s lymphoma. Manual histological grading of FL is subject to remarkable interand 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 nonrigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods.
This study proposes a method for the automatic nonrigid 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 with a proper feature set, the accuracy of automatic registration is comparable to manual registration.
Feature-Based Registration of Histopathology Images with Different Stains: An Application for Computerized Follicular Lymphoma Prognosis
Follicular lymphoma (FL) is the second most common type of non-Hodgkin’s lymphoma. Manual histological grading of FL is subject to remarkable interand 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 nonrigid image registration. However, the dissimilar appearances of the different stain types challenges existing registration methods.
This study proposes a method for the automatic nonrigid 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 with a proper feature set, the accuracy of automatic registration is comparable to manual registration.
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