An unsupervised clustering framework for automatic segmentation of left ventricle cavity in human heart angiograms.
Cardiac function is routinely assessed from X-rays angiograms acquired at the cardiac catheterization rooms. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction (EF). This evaluation requires the segmentation of the left ventricle (LV) contour. Several automatic segmentation methods have been reported, however, they are not yet fully validated and accepted in the clinical work. This paper reports on an automatic segmentation method for the ventricular cavity in mono-plane and bi-plane ventriculographic image sequences. The first step is the preprocessing, where a linear regression model is applied to exploit the functional relationship between the original input image and its smoothed version. A two stage clustering algorithm is used for segmenting the left ventricle cavity. First, an approximate initial segmentation is achieved by using a simple linkage region growing algorithm on the preprocessed version of the input image. The second stage is based on a region growing method by multiple linkage. This second stage is intended for refining the initial approximate segmentation. A validation is performed by comparing the estimated contours with respect to contours traced manually by several cardiologists. The average positioning error considering 15 mono-plane and 3 bi-plane angiographic sequences is 0.72mm at end-diastole (ED) and 0.91mm at end-systole (ES). The average contour error is 6.67% at ED and 12.44% at ES. The average area error is 8.58% at ED and 3.32% at ES. The left ventricle volume and the ejection fraction are estimated from manual contours and from the estimated contours showing an excellent correlation: 0.999 for ED volume, 0.998 for ES volume, and 0.952 for EF.
Bravo
Antonio
A
Universidad Nacional Experimental del Táchira, Decanato de Investigación, San Cristóbal 5001, Venezuela. abravo@unet.edu.ve
Medina
Rubén
R
D000465:Algorithms; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; 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