Abstract
Clinical practice in echotomography often requires effective and time-efficient 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.
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Bocchi, L., Rogai, F. (2012). A Genetic Fuzzy Rules Learning Approach for Unseeded Segmentation in Echography. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_31
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DOI: https://doi.org/10.1007/978-3-642-29178-4_31
Publisher Name: Springer, Berlin, Heidelberg
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