IEEE Trans Biomed Eng. 2011 Aug;58(8). doi: 10.1109/TBME.2011.2144982. Epub 2011 Apr 21.
Segmentation of echocardiographic images presents a great challenge because these images contain strong speckle noise and artifacts. Besides, most ultrasound segmentation methods are semi-automatic, requiring initial contour to be manually identified in the images. In this work, we propose an algorithm based on the phase symmetry approach and level set evolution, in order to extract simultaneously all heart cavities in a fully automatic way. The level set evolution uses a new logarithmic based stopping function, which demonstrates to perform well in the boundary extraction. We compared our method with other level set approaches, the watershed technique, and the manual segmentation made by two physicians. The experimental work was based on echocardiography images of children. Similarity metrics, namely Pratt Function, Pixel Mean Error, and Similarity Angle have been used for the performance evaluation of the different methods. The results indicate that our method has a performance at least 4% superior to the other methods able to segment the four chambers. Even for the two worst boundary extraction cases (right ventricle and left atrium) the performance of the proposed method still is better than the other techniques.
超声心动图图像的分割是一项极具挑战性的任务，因为这些图像中含有很强的斑点噪声和伪影。此外，大多数超声分割方法都是半自动的，需要在图像中手动确定初始轮廓。在这项工作中，我们提出了一种基于相位对称方法和水平集演化的算法，以便以全自动的方式同时提取所有的心脏腔室。水平集演化使用了一种新的基于对数的停止函数，该函数在边界提取中表现良好。我们将我们的方法与其他水平集方法、分水岭技术和两位医生的手动分割进行了比较。实验工作基于儿科超声心动图图像。相似性度量，即 Pratt 函数、像素平均误差和相似角，用于评估不同方法的性能。结果表明，我们的方法在分割四个腔室方面的性能至少比其他方法好 4%。即使在两个边界提取最差的情况下（右心室和左心房），所提出的方法的性能仍然优于其他技术。
