Using adaptive neuro-fuzzy inference systems for the detection of centroblasts in microscopic images of follicular lymphoma

In this paper, a complete methodology for automatic detection of centroblasts (CBs) in microscopic images acquired from tissue biopsies of follicular lymphoma is presented. In the proposed method, tissue sections are sliced at a low thickness level, around 1–1.5 \(\upmu \)m, which provides a more detailed depiction of the nuclei and other textural information. Initially, images are segmented into their basic cytological components, i.e., blood cells, nuclei and extra-cellular material, and then a novel touching-cell splitting algorithm is applied using a Gaussian mixture model and expectation–maximization algorithm. Additionally, a morphological and textural analysis of CBs is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. In the final step, a novel classification scheme is proposed based on adaptive neuro-fuzzy inference systems to classify the candidate cells. The methodology yielded promising results with an average detection rate of 90.35 %.