A new automated technique for left-and right-ventricular segmentation in magnetic resonance imaging.
In this paper we present a new automated method for detecting endocardial and epicardial borders in the left (LV) and right ventricles (RV) of the human heart. Our approach relies on morphological operations on both binary and grayscale images. First, the standard power-law transformation is applied on the image. Then, a region of interest (ROI) is selected semi-automatically, followed by automated endocardial and epicardial border extraction based on the selected ROI. In order to get the endocardial contour, the transformed image is thresholded and the maximum area, which indicates the cavity, is selected. Finally, the edge detection is performed and the papillary muscles (PMs) are excluded via a convex-hull method. The epicardial boundary is delineated through a threshold decomposition opening (TDO) approach along with morphological operations. The algorithm extracts the most precise myocardial and RV contours. Experimental results from three normal subjects are shown and quantitatively compared with manually traced contours by an expert. It is concluded that the method performs well in both endocardial and epicardial LV contouring as well as RV cavity detection.
Katouzian
Amin
A
Dept. of Biomed. Eng., Columbia Univ., New York, NY, USA. ak2432@columbia.edu
Prakash
Ashwin
A
Konofagou
Elisa
E
D000465:Algorithms; D001698:Biomedical Engineering; D004699:Endocardium / Q000033:anatomy & histology; D006321:Heart / Q000033:anatomy & histology*; D006352:Heart Ventricles / Q000033:anatomy & histology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D010496:Pericardium / Q000033:anatomy & histology