Open AccessComparison of three image segmentation techniques for target volume delineation in positron emission tomography
Author(s) -
Drever Laura,
Roa Wilson,
McEwan Alexander,
Robinson Don
Publication year - 2007
Publication title -
journal of applied clinical medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 48
ISSN - 1526-9914
DOI - 10.1120/jacmp.v8i2.2367
Subject(s) - positron emission tomography , sobel operator , thresholding , segmentation , imaging phantom , computer science , artificial intelligence , image segmentation , tomography , nuclear medicine , computer vision , image processing , edge detection , physics , image (mathematics) , medicine , optics
Incorporation of positron emission tomography (PET) data into radiotherapy planning is currently under investigation for numerous sites including lung, brain, head and neck, breast, and prostate. Accurate tumor‐volume quantification is essential to the proper utilization of the unique information provided by PET. Unfortunately, target delineation within PET currently remains a largely unaddressed problem. We therefore examined the ability of three segmentation methods—thresholding, Sobel edge detection, and the watershed approach—to yield accurate delineation of PET target cross‐sections. A phantom study employing well‐defined cylindrical and spherical volumes and activity distributions provided an opportunity to assess the relative efficacy with which the three approaches could yield accurate target delineation in PET. Results revealed that threshold segmentation can accurately delineate target cross‐sections, but that the Sobel and watershed techniques both consistently fail to correctly identify the size of experimental volumes. The usefulness of threshold‐based segmentation is limited, however, by the dependence of the correct threshold (that which returns the correct area at each image slice) on target size. PACS numbers: 87.58.Fg, 87.57.Nk