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Comparative evaluation of image segmentation methods for volume quantitation in SPECT
Author(s) -
Long David T.,
King Michael A.,
Sheehan John
Publication year - 1992
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.596837
Subject(s) - segmentation , artificial intelligence , computer vision , image segmentation , volume (thermodynamics) , computer science , edge detection , image processing , medical imaging , contrast (vision) , nuclear medicine , mathematics , pattern recognition (psychology) , image (mathematics) , medicine , physics , quantum mechanics
A wide variety of image segmentation techniques have been proposed for the measurement of organ or lesion volumes in SPECT images. Evaluation of the relative performance of the various methods is difficult due to wide variations in system response characteristics, size, shape, and contrast of the imaged objects, and image acquisition and processing techniques. Selected image segmentation methods for volume quantitation in SPECT were applied to a set of simulated SPECT images containing objects ranging in volume from 1.8 to 113.1 cc. The specific segmentation methods included: (1) operator drawn regions of interest, (2) count‐based methods, (3) three levels of fixed thresholds, (4) an adaptive threshold (GLH method), (5) a two‐dimensional (2‐D) edge detection method, and (6) a three‐dimensional (3‐D) edge detection method. In general, the 3‐D edge detection method required minimal operator intervention while providing the most accurate and consistent estimates of object volume across changes in object contrast and size.