在利用实时3维超声心动图诊断心脏病的过程中，医生手动寻找各关键剖视面既繁琐又费时；四腔切面是最重要的观察切面之一，为便于诊断，提出了基于模板匹配的自动检测3维超声心动图四腔切面的方法。首先，在医生指导下选择一幅四腔切面图像作为模板；其次，从3维心动图中提取一系列切面建立待检索图像库；最后，用两种不同的相似测度依次进行粗检索和精检索，找到与模板图像最相似的切面，即为该3维心动图中的四腔切面。利用该方法对28组正常人数据和22组非正常人数据进行实验，正确率分别为96%及863%。该方法仅利用一幅模板图像，初步实现了不同人3维超声心动图四腔切面的自动检测；其计算复杂度低，易于实现实时处理，避免了人工寻找切面的诸多弊端，对测量及配准等后续的计算机辅助诊断有重要意义。

In clinical heart disease diagnosis by real-time 3D echocardiography, it is time-consuming and tedious for doctors to manually search the best views to detect the complex cardiac anatomy, when they routinely check a large amount of patients 3D echocardiographic images. Since the apical four-chamber（a4c) view is one of the most important screening planes, the purpose of this study is to detect the a4c plane in the 3D echocardiographic image automatically. Firstly, a typical a4c image is chosen as a template image. Then to find the a4c image in a 3D echocardiographic volume data, a series of cross-sections are extracted from this data to build an ultrasound image database. Via a coarse to fine retrieval, the cross-section in the database most similar to the template is taken as the a4c image in this 3D data. Tested on 28 datasets of normal children and 22 datasets of children suffering from congenital heart disease, this method achieved the accuracy rates of 96% and 86.3%, respectively. By only one template image, this method can detect the a4c image planes in the echocardiographic volumetric datasets of different subjects. With low computational complexity and simple implementation, the proposed method gives promising results for applying the auto-detection of the a4c view to clinical diagnosis, which is significant for following computer-aided diagnosis approach such as registration and measurement.