Genetic Adaptation of Level Sets Parameters for Medical Imaging Segmentation

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Author(s): Dário A.B. Oliveira (Catholic University of Rio de Janeiro, Brazil), Raul Q. Feitosa (Catholic University of Rio de Janeiro, Brazil)and Mauro M. Correia (Unigranrio and National Cancer Institute-INCA, Brazil)
Copyright: 2010
Pages: 17
Source title: Biomedical Image Analysis and Machine Learning Technologies: Applications and Techniques
Source Author(s)/Editor(s): Fabio A. Gonzalez (National University of Colombia, Colombia )and Eduardo Romero (National University of Colombia, Colombia )
DOI: 10.4018/978-1-60566-956-4.ch007

Keywords: Computer Science & IT / Computer Vision & Image Processing / Medical Information Science Reference / Medical Technologies

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Abstract

This chapter presents a method based on level sets to segment organs using computer tomography (CT) medical images. Initially, the organ boundary is manually set in one slice as an initial solution, and then the method automatically segments the organ in all other slices, sequentially. In each step of iteration it fits a Gaussian curve to the organ’s slice histogram to model the speed image in which the level sets propagate. The parameters of our method are estimated using genetic algorithms (GA) and a database of reference segmentations. The method was tested to segment the liver using 20 different exams and five different measures of performance, and the results obtained confirm the potential of the method. The cases in which the method presented a poor performance are also discussed in order to instigate further research.

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Genetic Adaptation of Level Sets Parameters for Medical Imaging Segmentation

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