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Proceedings Paper

Follicular lymphoma grading using cell-graphs and multi-scale feature analysis
Author(s): Basak Oztan; Hui Kong; Metin N. Gürcan; Bülent Yener
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Paper Abstract

We present a method for the computer-aided histopathological grading of follicular lymphoma (FL) images based on a multi-scale feature analysis. We analyze FL images using cell-graphs to characterize the structural organization of the cells in tissues. Cell-graphs represent histopathological images with undirected and unweighted graphs wherein the cytological components constitute the graph nodes and the approximate adjacencies of the components are represented with edges. Using the features extracted from nuclei- and cytoplasm-based cell-graphs, a classifier defines the grading of the follicular lymphoma images. The performance of this system is comparable to that of our recently developed system that characterizes higher-level semantic description of tissues using model-based intermediate representation (MBIR) and color-textural analysis. When tested with three different classifiers, the combination of cell-graph based features with the MBIR and color-textural features followed by a multi-scale feature selection is shown to achieve considerably higher classification accuracies than any set of these feature sets can achieve separately.

Paper Details

Date Published: 23 February 2012
PDF: 9 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831516 (23 February 2012); doi: 10.1117/12.911360
Show Author Affiliations
Basak Oztan, Rensselaer Polytechnic Institute (United States)
Hui Kong, The Ohio State Univ. Medical Ctr. (United States)
Metin N. Gürcan, The Ohio State Univ. Medical Ctr. (United States)
Bülent Yener, Rensselaer Polytechnic Institute (United States)


Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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