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Classification of centroblast cells in follicular lymphoma method accepted for publication in the Analytical  and Quantitative Cytology and Histology

A method for the classification of Centrablast cells versus Non-Centroblast in Follicular Lymphoma has been accepted for publication in the Analytical and Quantitative Cytology and Histology journal. The authors of the paper were Kamel Belkacem-Boussaid, Ph.D, and Michael. Pennell, Ph.D, Gerard. Lozanski, MD, Arwa. Shanaah, MD, and Metin Gurcan, Ph.D. The title of the manuscript is "Computer-aided classification of centroblast cells in follicular lymphoma". The abstract of the manuscript appears below.

Objective:
In this paper, we develop a novel automated method to distinguish centroblast (CB) cells from non-centroblast (non-CB) cells in follicular lymphoma cases and measure its performance on cases obtained by a consensus of six pathologists.

Study Design:
Geometric and color texture features were used in the training and testing of the supervised quadratic discriminant analysis (QDA) classifier. The technique was trained and tested on a data set composed of 218 CB images and 218 non-CB images. Computer performance was tested by measuring sensitivity and specificity among cells classified as centroblasts and non-centroblasts by consensus of six board-certified hematopathologists.

Results and Conclusion:
Automated classification distinguished centroblast cells (CB) from non-centroblast cells (non-CB) with a classification accuracy of 82.56% and sensitivity and specificity were 86.67% and 86.96%, respectively, when the approach was tested. The novelty of our approach is the identification of the CB cells with prior information, and the introduction of the principal component analysis (PCA) in the spectral domain to extract texture color features.

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