An evolutionary Michigan recurrent fuzzy system for nuclei classification in cytological images using nuclear chromatin distribution

ObjectiveThe objective of this research is to carry out the classification of cellular nuclei in cytological pleural fluid images. The article focuses on the feature extraction and classification processes. The extracted feature is a spatial measurement of the chromatin distribution in cellular nuclei. The designed classifiers are fuzzy classifiers that carry out supervised classification. The classifier system’s inputs are data series that represent these texture measurements.Methods and materialThe classifier is built on a Recurrent Fuzzy System (RFS). An evolutionary algorithm inspired by the Michigan approach is used to find an optimal RFS to classify different patterns expressed as data series.ResultsThe effectiveness of the proposed classifier system is compared with other existing classification methods and evaluated via Receiver Operating Characteristic (ROC) analysis. We have obtained RFS based classifiers that perform with sensitivity values between 82.26 and 93.55% and with specificity values between 80.65 and 90.32%. The behavior of the proposed chromatin measurement is also compared with other texture measurements.ConclusionThe RFS based classifiers were successfully applied to the proposed data series that represent the chromatin distribution in cellular nuclei. These fuzzy classifiers present the highest classification efficiency and the ROC analysis confirms their suitable behavior.