An Image-Based Comprehensive Approach for Automatic Segmentation of Left Ventricle from Cardiac Short Axis Cine MR Images
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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.
Key wordsImage segmentation cardiac imaging image analysis left ventricle
We thank Sunnybrook Health Sciences Centre for making their clinical image data, ground truth contour data and evaluation software accessible to public.
We gratefully acknowledge funding for this research by the Biomedical Research Council, Agency for Science, Technology and Research, Singapore.
- 1.Selvanayagam JB, Robson MD, Francis JM, Neubauer S: Cardiovascular Magnetic Resonance: Basic Principles, Methods and Techniques. In: Dilsizian V, Pohost GM Eds. Cardiac CT, PET and MRI. Blackwell, Oxford, 2007, pp 28–68Google Scholar
- 2.Lu Y, Radau P, Connelly K, Dick A, Wright GA: Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method: LNCS 5528:339–347, 2008Google Scholar
- 13.Boykov Y, Jolly M-P: Interactive Organ Segmentation Using Graph Cuts. Proceedings of MICCAI, 2000, pp 276–286Google Scholar
- 14.Lin X, Cowan B, Young A: Model-Based Graph Cut Method for Segmentation of the Left Ventricle. 27th Annual International Conference Proceedings of the Engineering in Medicine and Biology Society, IEEE-EMBS, 2005, pp 3059–3062Google Scholar
- 18.Liao PS, Chen TS, Chung PC: A fast algorithm for multilevel thresholding. J Inf Sci Eng 17:713–727, 2001Google Scholar