TY - JOUR
T1 - A method for automatic edge detection and volume computation of the left ventricle from ultrafast computed tomographic images
AU - Dove, Edwin L.
AU - Philip, Karun
AU - Gotteiner, Nina L.
AU - Vonesh, Michael J.
AU - Rumberger, John A.
AU - Reed, Judd E.
AU - Stanford, William
AU - Mc pherson, David D.
AU - Chandran, Krishnan B.
PY - 1994/11
Y1 - 1994/11
N2 - Rationale and Objectives. Detection of endocardial and epicardial borders of the left ventricle (LV) using various imaging modalities is time-consuming and prone to interpretive error. An automatic border detection algorithm is presented that is used with ultrafast computed tomographic images of the heart to compute cavity volumes. Methods. The basal-level slice is identified, and the algorithm automatically detects the endocardial and epicardial borders of images from the basal to the apical levels. From these, the ventricular areas and chamber volumes are computed. The algorithm uses the Fuzzy Hough Transform, region-growing schemes, and optimal border-detection techniques. The cross-sectional areas and the chamber volumes computed with this technique were compared with those from manually traced images using canine hearts in vitro (n = 8) and studies in clinical patients (n = 27). Results. Though the correlation was good (r = .88), the algorithm overestimated the LV epicardial area by 4.8 ± 6.4 cm2, though this error was not statistically different from zero (P > .05). There was no difference in endocardial areas (r = .95, P > .05). The algorithm tended to underestimate the end-diastolic volume (r = .94) and the end-systolic volume (r = .94), although these errors were not statistically different from zero (P > .05). The algorithm tended to underestimate the ejection fraction (r = .80), although this error was not statistically different from zero (P > .05). Conclusions. Automatic detection of myocardial borders provides the clinician with a useful tool for calculating chamber volumes and ejection fractions. The algorithm, with the corrections suggested, provides an accurate estimation of areas and volumes. This algorithm may be useful for contour border identification with ultrasound, positron-emission tomography, magnetic resonance imaging, and other imaging modalities in the heart, as well as other structures.
AB - Rationale and Objectives. Detection of endocardial and epicardial borders of the left ventricle (LV) using various imaging modalities is time-consuming and prone to interpretive error. An automatic border detection algorithm is presented that is used with ultrafast computed tomographic images of the heart to compute cavity volumes. Methods. The basal-level slice is identified, and the algorithm automatically detects the endocardial and epicardial borders of images from the basal to the apical levels. From these, the ventricular areas and chamber volumes are computed. The algorithm uses the Fuzzy Hough Transform, region-growing schemes, and optimal border-detection techniques. The cross-sectional areas and the chamber volumes computed with this technique were compared with those from manually traced images using canine hearts in vitro (n = 8) and studies in clinical patients (n = 27). Results. Though the correlation was good (r = .88), the algorithm overestimated the LV epicardial area by 4.8 ± 6.4 cm2, though this error was not statistically different from zero (P > .05). There was no difference in endocardial areas (r = .95, P > .05). The algorithm tended to underestimate the end-diastolic volume (r = .94) and the end-systolic volume (r = .94), although these errors were not statistically different from zero (P > .05). The algorithm tended to underestimate the ejection fraction (r = .80), although this error was not statistically different from zero (P > .05). Conclusions. Automatic detection of myocardial borders provides the clinician with a useful tool for calculating chamber volumes and ejection fractions. The algorithm, with the corrections suggested, provides an accurate estimation of areas and volumes. This algorithm may be useful for contour border identification with ultrasound, positron-emission tomography, magnetic resonance imaging, and other imaging modalities in the heart, as well as other structures.
KW - Automatic edge detection
KW - Fuzzy hough transform
KW - Left ventricular area and volumes
KW - Region-growing algorithm
KW - Ultrafast computed tomography
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U2 - 10.1097/00004424-199411000-00001
DO - 10.1097/00004424-199411000-00001
M3 - Article
C2 - 7890509
AN - SCOPUS:0028605497
SN - 0020-9996
VL - 29
SP - 945
EP - 954
JO - Investigative Radiology
JF - Investigative Radiology
IS - 11
ER -