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.
DOI
10.1016/j.neucom.2015.09.123
Persistent Identifier
http://archives.pdx.edu/ds/psu/16861
Citation Details
Ghosh, Payel; Mitchell, Melanie; Tanyi, James A.; and Hung, Arthur Y., "Incorporating Priors for Medical Image Segmentation Using a Genetic Algorithm" (2016). Computer Science Faculty Publications and Presentations. 150.
http://archives.pdx.edu/ds/psu/16861
Description
This is the post-print version. The final publisher's version is available here:
http://www.sciencedirect.com/science/article/pii/S0925231216001065
© 2016, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/)