Iterative threshold segmentation for PET target volume delineation

The purpose of this work is to create a rigorous method of segmenting PET images using an automated iterative technique. To this end a phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast be...

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Vydáno v:Medical physics (Lancaster) Ročník 34; číslo 4; s. 1253 - 1265
Hlavní autoři: Drever, Laura, Roa, Wilson, McEwan, Alexander, Robinson, Don
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States American Association of Physicists in Medicine 01.04.2007
Témata:
ACCURACY
ALGORITHMS
Ancillary equipment
Artificial Intelligence
Cancer
Computed tomography
CONCENTRATION RATIO
Contrast sensitivity
CROSS SECTIONS
Humans
Image analysis
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
IMAGE PROCESSING
Image quality
image resolution
image segmentation
Imaging, Three-Dimensional - methods
iterative
ITERATIVE METHODS
Medical image contrast
Medical image noise
medical image processing
Medical imaging
Numerical Analysis, Computer-Assisted
Numerical approximation and analysis
Pattern Recognition, Automated - methods
PET
PHANTOMS
Phantoms, Imaging
POSITRON COMPUTED TOMOGRAPHY
positron emission tomography
Positron emission tomography (PET)
Positron-Emission Tomography - instrumentation
Positron-Emission Tomography - methods
Radiation therapy
RADIOLOGY AND NUCLEAR MEDICINE
Reproducibility of Results
Sensitivity and Specificity
target volume
threshold segmentation
threshold segmentation
iterative
target volume
PET
ISSN:0094-2405, 2473-4209
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Popis
Shrnutí:The purpose of this work is to create a rigorous method of segmenting PET images using an automated iterative technique. To this end a phantom study employing spherical targets was used to determine local (slice specific) threshold levels which produce correct cross-sections based on the contrast between target and background. Numerous target to background activity concentration ratios were investigated but found to have minimal effect in comparison to the influence of target size. Functions were fit to this data and used to construct an iterative threshold segmentation algorithm. In all cases this approach yielded convergence within ten iterations. Iterative threshold segmentation was applied using both an axial and tri-axial approach to the spherical targets and also to two irregularly shaped volumes. Of these two approaches, the tri-axial method proved less susceptible to image noise and better at dealing with partial volume effects at the interface between target and background. For comparative purposes, single thresholds of 28% and 40% were also applied to the spherical data sets. The tri-axial iterative method was found capable of delineating cross sections with areas greater than 250 mm 2 to within the maximum resolution possible ( 1 pixel width). Cross sections of less than 250 mm 2 in area were resolved by the tri-axial method to within 2 pixel widths of their true physical extent. Local contrast based iterative threshold segmentation shows promise as a method of rigorously delineating PET target volumes with good accuracy subject to the limitations imposed by the image resolution which currently characterizes this modality.
Bibliografie:don.robinson@cancerboard.ab.ca
Author to whom correspondence should be addressed. Electronic mail
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ISSN:0094-2405
2473-4209
DOI:10.1118/1.2712043