Duta N, Sonka M
Department of Computer Science, Michigan State University, East Lansing 48823, USA.
IEEE Trans Med Imaging. 1998 Dec;17(6):1049-62. doi: 10.1109/42.746716.
This paper reports a novel method for fully automated segmentation that is based on description of shape and its variation using point distribution models (PDM's). An improvement of the active shape procedure introduced by Cootes and Taylor to find new examples of previously learned shapes using PDM's is presented. The new method for segmentation and interpretation of deep neuroanatomic structures such as thalamus, putamen, ventricular system, etc. incorporates a priori knowledge about shapes of the neuroanatomic structures to provide their robust segmentation and labeling in magnetic resonance (MR) brain images. The method was trained in eight MR brain images and tested in 19 brain images by comparison to observer-defined independent standards. Neuroanatomic structures in all testing images were successfully identified. Computer-identified and observer-defined neuroanatomic structures agreed well. The average labeling error was 7%+/-3%. Border positioning errors were quite small, with the average border positioning error of 0.8+/-0.1 pixels in 256 x 256 MR images. The presented method was specifically developed for segmentation of neuroanatomic structures in MR brain images. However, it is generally applicable to virtually any task involving deformable shape analysis.
本文报告了一种基于使用点分布模型（PDM）描述形状及其变化的全自动分割新方法。提出了对Cootes和Taylor引入的主动形状程序的改进，以使用PDM找到先前学习形状的新示例。用于分割和解释丘脑、壳核、脑室系统等深部神经解剖结构的新方法结合了关于神经解剖结构形状的先验知识，以在磁共振（MR）脑图像中对其进行稳健的分割和标记。该方法在八幅MR脑图像中进行了训练，并通过与观察者定义的独立标准进行比较，在19幅脑图像中进行了测试。所有测试图像中的神经解剖结构均被成功识别。计算机识别的和观察者定义的神经解剖结构吻合良好。平均标记误差为7%±3%。边界定位误差非常小，在256×256的MR图像中，平均边界定位误差为0.8±0.1像素。所提出的方法是专门为MR脑图像中的神经解剖结构分割而开发的。然而，它通常适用于几乎任何涉及可变形形状分析的任务。
