A Genetic Fuzzy Rules Learning Approach for Unseeded Segmentation in Echography

  • Leonardo Bocchi
  • Francesco Rogai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

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.

Keywords

Membership Function Cellular Automaton Fuzzy Rule Fuzzy Inference System Active Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leonardo Bocchi
    • 1
  • Francesco Rogai
    • 1
  1. 1.Dipartimento di Elettronica e TelecomunicazioniUniversitá degli Studi di FirenzeItaly

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