Arias Pablo, Pini Alejandro, Sanguinetti Gonzalo, Sprechmann Pablo, Cancela Pablo, Fernández Alicia, Gómez Alvaro, Randall Gregory
Instituto de Ingeniería Eléctrica, Universidad de la República, Montevideo 11300, Uruguay.
IEEE Trans Image Process. 2007 Jun;16(6):1637-45. doi: 10.1109/tip.2007.896604.
Automatic ultrasound (US) image segmentation is a difficult task due to the quantity of noise present in the images and the lack of information in several zones produced by the acquisition conditions. In this paper, we propose a method that combines shape priors and image information to achieve this task. In particular, we introduce knowledge about the rib-eye shape using a set of images manually segmented by experts. A method is proposed for the automatic segmentation of new samples in which a closed curve is fitted taking into account both the US image information and the geodesic distance between the evolving curve and the estimated mean rib-eye shape in a shape space. This method can be used to solve similar problems that arise when dealing with US images in other fields. The method was successfully tested over a database composed of 610 US images, for which we have the manual segmentations of two experts.
由于超声（US）图像中存在大量噪声以及采集条件导致的多个区域信息缺失，自动超声图像分割是一项艰巨的任务。在本文中，我们提出了一种结合形状先验和图像信息来完成此任务的方法。具体而言，我们利用一组由专家手动分割的图像引入关于肋眼形状的知识。提出了一种用于新样本自动分割的方法，其中在考虑超声图像信息以及形状空间中演化曲线与估计的平均肋眼形状之间的测地距离的情况下拟合一条封闭曲线。该方法可用于解决处理其他领域的超声图像时出现的类似问题。该方法在由610幅超声图像组成的数据库上成功进行了测试，我们拥有两位专家对这些图像的手动分割结果。
