Incorporating priors for medical image segmentation using a genetic algorithm

Payel Ghosh, Melanie Mitchell, James A. Tanyi, Arthur Y. Hung

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Medical image segmentation is typically performed manually by a physician to delineate gross tumor volumes for treatment planning and diagnosis. Manual segmentation is performed by medical experts using prior knowledge of organ shapes and locations but is prone to reader subjectivity and inconsistency. Automating the process is challenging due to poor tissue contrast and ill-defined organ/tissue boundaries in medical images. This paper presents a genetic algorithm for combining representations of learned information such as known shapes, regional properties and relative position of objects into a single framework to perform automated three-dimensional segmentation. The algorithm has been tested for prostate segmentation on pelvic computed tomography and magnetic resonance images.

Original languageEnglish (US)
Pages (from-to)181-194
Number of pages14
StatePublished - Jun 26 2016


  • Genetic Algorithms
  • Medical image segmentation
  • Relative position priors
  • Shape Modeling
  • Texture Analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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