Seng Cher Hau, Demirli Ramazan, Amin Moeness G, Seachrist Jason L, Bouzerdoum Abdesselam
School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7215-8. doi: 10.1109/IEMBS.2011.6091823.
The accurate left ventricular boundary detection in echocardiographic images allow cardiologists to study and assess cardiomyopathy in patients. Due to the tedious and time consuming manner of manually tracing the borders, deformable models are generally used for left ventricle segmentations. However, most deformable models require a good initialization, which is usually outlined manually by the user. In this paper, we propose an automated left ventricle detection method for two-dimensional echocardiographic images that could serve as an initialization for deformable models. The proposed approach consists of pre-processing and post-processing stages, coupled with the watershed segmentation. The pre-processing stage enhances the overall contrast and reduces speckle noise, whereas the post-processing enhances the segmented region and avoids the papillary muscles. The performance of the proposed method is evaluated on real data. Experimental results show that it is suitable for automatic contour initialization since no prior assumptions nor human interventions are required. Besides, the computational time taken is also lower compared to an existing method.
超声心动图图像中准确的左心室边界检测有助于心脏病专家研究和评估患者的心肌病。由于手动追踪边界既繁琐又耗时，因此通常使用可变形模型进行左心室分割。然而，大多数可变形模型需要良好的初始化，这通常由用户手动勾勒。在本文中，我们提出了一种用于二维超声心动图图像的自动左心室检测方法，该方法可作为可变形模型的初始化。所提出的方法包括预处理和后处理阶段，并结合分水岭分割。预处理阶段增强了整体对比度并减少了斑点噪声，而后处理增强了分割区域并避免了乳头肌。该方法的性能在真实数据上进行了评估。实验结果表明，它适用于自动轮廓初始化，因为不需要先验假设和人工干预。此外，与现有方法相比，其计算时间也更短。
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