title: Left ventricular myocardium segmentation on arterial phase of multi-detector row computed tomography.
abstract: OBJECTIVE
  Variation of left ventricular myocardial volumes correlates closely with ischemic heart diseases. In clinical practice, because physicians and radiologists rely much on myocardial contour to diagnose many different cardiac diseases, automatic segmentation of left ventricular myocardium and quantifying myocardium characteristics is clinically beneficial. This paper presents a hybrid segmentation method for left ventricular myocardium on arterial phase of multi-detector row computed tomography (MDCT) imaging.
  
  
  METHODS
  The proposed method utilizes an intensity transformation equation as a preprocessing procedure to enhance contrast and reduce noise in MDCT imaging. By setting the centroid of left ventricle (LV) as an initial seed, the conventional region growing method is employed to identify the endocardial contour of LV cavity for each slice. Then the level-set method (LSM) utilizes the extracted endocardial contour as initial contour to delineate the epicardium of LV. The two extracted contours are integrated to form the region of interest (ROI) of the LV. Finally, the ROIs from all slices are combined to obtain the volume of the whole LV myocardium.
  
  
  RESULTS
  Twenty-two healthy patients who had no symptoms of ischemic heart disease are applied to evaluate the performance of the proposed method. Compared with manual contours delineated by two experienced experts, the contouring results from computer simulation reveal that the proposed method always identifies similar contours as that obtained by the manual sketching.
  
  
  CONCLUSIONS
  The proposed method provides a robust and fast automatic contouring for LV myocardium on arterial phase of MDCT. The potential role of this technique may save much of the time required to manually sketch a precise contour with high stability.
authors.affiliation: Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan.
authors.forename: I-Chen
authors.initials: IC
authors.lastname: Tsai
authors.forename: Yu-Len
authors.initials: YL
authors.lastname: Huang
authors.forename: Kai-Hua
authors.initials: KH
authors.lastname: Kuo
mesh_terms: D000465:Algorithms; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*