A genetic fuzzy rules learning approach for unseeded segmentation in echography

Clinical practice in echotomography often requires effective and time-effcient procedures for segmenting anatomical structures to take medical decisions for therapy and diagnosis. In this work we present a methodology for image segmentation in echography with the aim to assist the clinician in these delicate tasks. A generic segmentation algorithm, based on region evaluation by means of a fuzzy rules based inference system (FRBS), is refined in a fully unseeded segmentation algorithm. Rules composing knowledge base are learned with a genetic algorithm, by comparing computed segmentation with human expert segmentation. Generalization capabilities of the approach are assessed with a larger test set and over different applications: breast lesions, ovarian follicles and anesthetic detection during brachial anesthesia