Published In

Neurocomputing

Document Type

Post-Print

Publication Date

2-4-2016

Subjects

Diagnostic imaging, Genetic Algorithms

Abstract

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.

Description

This is the post-print version. The final publisher's version is available here:
http://www.sciencedirect.com/science/article/pii/S0925231216001065

DOI

10.1016/j.neucom.2015.09.123

Persistent Identifier

http://archives.pdx.edu/ds/psu/16861

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