An image analysis approach for detecting malignant cells in digitized H&E-stained histology images of follicular lymphoma

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.