Department of Electrical Engineering, University of Brasília, Brasília, DF, Brazil.
Biomed Eng Online. 2010 Jan 15;9:5. doi: 10.1186/1475-925X-9-5.
Two-dimensional echocardiography (2D-echo) allows the evaluation of cardiac structures and their movements. A wide range of clinical diagnoses are based on the performance of the left ventricle. The evaluation of myocardial function is typically performed by manual segmentation of the ventricular cavity in a series of dynamic images. This process is laborious and operator dependent. The automatic segmentation of the left ventricle in 4-chamber long-axis images during diastole is troublesome, because of the opening of the mitral valve.
This work presents a method for segmentation of the left ventricle in dynamic 2D-echo 4-chamber long-axis images over the complete cardiac cycle. The proposed algorithm is based on classic image processing techniques, including time-averaging and wavelet-based denoising, edge enhancement filtering, morphological operations, homotopy modification, and watershed segmentation. The proposed method is semi-automatic, requiring a single user intervention for identification of the position of the mitral valve in the first temporal frame of the video sequence. Image segmentation is performed on a set of dynamic 2D-echo images collected from an examination covering two consecutive cardiac cycles.
The proposed method is demonstrated and evaluated on twelve healthy volunteers. The results are quantitatively evaluated using four different metrics, in a comparison with contours manually segmented by a specialist, and with four alternative methods from the literature. The method's intra- and inter-operator variabilities are also evaluated.
The proposed method allows the automatic construction of the area variation curve of the left ventricle corresponding to a complete cardiac cycle. This may potentially be used for the identification of several clinical parameters, including the area variation fraction. This parameter could potentially be used for evaluating the global systolic function of the left ventricle.
二维超声心动图（2D-echo）可用于评估心脏结构及其运动。许多临床诊断都基于左心室的表现。心肌功能的评估通常通过手动分割一系列动态图像中的心室腔来完成。这个过程既繁琐又依赖于操作者。由于二尖瓣的开放，舒张期时在四腔心长轴图像中自动分割左心室比较麻烦。
本研究提出了一种在完整心动周期的动态 2D-echo 四腔心长轴图像中分割左心室的方法。所提出的算法基于经典图像处理技术，包括时间平均和基于小波的去噪、边缘增强滤波、形态学操作、同伦修正和分水岭分割。该方法是半自动的，仅需要用户在视频序列的第一帧中手动识别二尖瓣的位置。图像分割是在一组从覆盖两个连续心动周期的检查中采集的动态 2D-echo 图像上进行的。
该方法在 12 名健康志愿者中进行了演示和评估。结果使用四种不同的度量标准进行定量评估，与由专家手动分割的轮廓进行比较，并与文献中的四种替代方法进行比较。还评估了该方法的内和间操作者变异性。
所提出的方法允许自动构建与完整心动周期相对应的左心室面积变化曲线。这可能潜在地用于识别几个临床参数，包括面积变化分数。该参数可用于评估左心室的整体收缩功能。
