Marsousi Mahdi, Alirezaie Javad, Ahmadian Alireza, Kocharian Armen
Research Center for Science and Technology in Medicine, RCSTIM, Tehran, Iran.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:3125-8. doi: 10.1109/IEMBS.2010.5626094.
In this paper, a fully automated method for segmenting Left Ventricle (LV) in echocardiography images is proposed. A new method named active ellipse model is developed to automatically find the best ellipse inside the LV chamber without intervention of any specialist. A modified B-Spline Snake algorithm is used to segment the LV chamber in which the initial contour is formed by the predefined ellipse. As a result of using active ellipse model, the segmentation is extricated from dealing with gaps within myocardium boundary which are highly problematic in echocardiography image segmentation. Based on the results obtained from different studies, the proposed method is faster and more accurate than previous approaches. Our method is evaluated on 20 sets of echocardiography images of patients; and acquired results (92.30 ± 4.45% dice's coefficient) indicate the proposed method has remarkable performance.
本文提出了一种用于超声心动图图像中左心室（LV）分割的全自动方法。开发了一种名为主动椭圆模型的新方法，可在无需任何专家干预的情况下自动在左心室腔内找到最佳椭圆。使用改进的B样条蛇算法对左心室腔进行分割，其中初始轮廓由预定义的椭圆形成。由于使用了主动椭圆模型，分割过程摆脱了处理心肌边界内间隙的问题，而这些间隙在超声心动图图像分割中是非常棘手的。基于不同研究获得的结果，所提出的方法比以前的方法更快、更准确。我们的方法在20组患者的超声心动图图像上进行了评估；获得的结果（骰子系数为92.30±4.45%）表明所提出的方法具有显著的性能。
