An Image Analysis Approach for Detecting Malignant Cells in Digitized H&E-stained Histology Images of Follicular Lymphoma
Olcay Sertel, Ümit V. Çatalyürek, Gerard Lozanski, Arwa Shana’ah, Metin N. Gürcan · 2010
The gold standard in follicular lymphoma (FL) diagnosis and prognosis is histopathological examination of tumor tissue samples. However, the qualitative manual evaluation is tedious and subject to considerable inter- and intra-reader variations. In this study, we propose an image analysis system for quantitative evaluation of digitized FL tissue slides. The developed system uses a robust feature space analysis method, namely the mean shift algorithm followed by a hierarchical grouping to segment a given tissue image into basic cytological components. We then apply further morphological operations to achieve the segmentation of individual cells. Finally, we generate a likelihood measure to detect candidate cancer cells using a set of clinically driven features. The proposed approach has been evaluated on a dataset consisting of 100 region of interest (ROI) images and achieves a promising 89% average accuracy in detecting target malignant cells.