title: An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images.
abstract: Segmentation of the left ventricle is important in the assessment of cardiac functional parameters. Manual segmentation of cardiac cine MR images for acquiring these parameters is time-consuming. Accuracy and automation are the two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach to segment the left ventricle from short axis cine cardiac MR images automatically. Our method incorporates a number of image processing and analysis techniques including thresholding, edge detection, mathematical morphology, and image filtering to build an efficient process flow. This process flow makes use of various features in cardiac MR images to achieve high accurate segmentation results. Our method was tested on 45 clinical short axis cine cardiac images and the results are compared with manual delineated ground truth (average perpendicular distance of contours near 2 mm and mean myocardium mass overlapping over 90%). This approach provides cardiac radiologists a practical method for an accurate segmentation of the left ventricle.
authors.affiliation: Biomedical Imaging Laboratory, Singapore Bio-imaging Consortium, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore. huangsu@sbic.a-star.edu.sg
authors.forename: Su
authors.initials: S
authors.lastname: Huang
authors.forename: Jimin
authors.initials: J
authors.lastname: Liu
authors.forename: Looi Chow
authors.initials: LC
authors.lastname: Lee
authors.forename: Sudhakar K
authors.initials: SK
authors.lastname: Venkatesh
authors.forename: Lynette Li San
authors.initials: LL
authors.lastname: Teo
authors.forename: Christopher
authors.initials: C
authors.lastname: Au
authors.forename: Wieslaw L
authors.initials: WL
authors.lastname: Nowinski
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D018487:Ventricular Dysfunction, Left / Q000175:diagnosis* / Q000503:physiopathology