title: A method for automatic edge detection and volume computation of the left ventricle from ultrafast computed tomographic images.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City 52245.
authors.forename: E L
authors.initials: EL
authors.lastname: Dove
authors.forename: K
authors.initials: K
authors.lastname: Philip
authors.forename: N L
authors.initials: NL
authors.lastname: Gotteiner
authors.forename: M J
authors.initials: MJ
authors.lastname: Vonesh
authors.forename: J A
authors.initials: JA
authors.lastname: Rumberger
authors.forename: J E
authors.initials: JE
authors.lastname: Reed
authors.forename: W
authors.initials: W
authors.lastname: Stanford
authors.forename: D D
authors.initials: DD
authors.lastname: McPherson
authors.forename: K B
authors.initials: KB
authors.lastname: Chandran
mesh_terms: D000465:Algorithms; D000818:Animals; D002306:Cardiac Volume; D003971:Diastole; D004285:Dogs; D004699:Endocardium / Q000000981:diagnostic imaging; D017143:Fuzzy Logic; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D010496:Pericardium / Q000000981:diagnostic imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D013318:Stroke Volume; D013599:Systole; D014057:Tomography, X-Ray Computed / Q000379:methods*; D016277:Ventricular Function, Left