An ensemble method for nuclei detection of overlapping cervical cells - Diniz, Débora Nasser; Vitor, Rafael Ferreira; Bianchi, Andrea Gomes Campos; Delabrida, Saul; Carneiro, Cláudia Martins; Ushizima, Daniela Mayumi; de Medeiros, Fátima Nelsizeuma Sombra; Souza, Marcone Jamilson Freitas (Expert systems with applications, 2021)
The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988). [Display omitted] •Ensemble of classifiers to provide a more assertive cervical cell nuclei segmentation.•Automatic inspection of Pap smear test to reduce the human workload in cervical cell analysis.•Superpixel computation and feature extraction to improve cell characterization and classification.