de Bruijne Marleen, van Ginneken Bram, Viergever Max A, Niessen Wiro J
Image Sciences Institute, University Medical Center Utrecht, The Netherlands.
Inf Process Med Imaging. 2003 Jul;18:136-47. doi: 10.1007/978-3-540-45087-0_12.
Active Shape Models (ASM) have proven to be an effective approach for image segmentation. In some applications, however, the linear model of gray level appearance around a contour that is used in ASM is not sufficient for accurate boundary localization. Furthermore, the statistical shape model may be too restricted if the training set is limited. This paper describes modifications to both the shape and the appearance model of the original ASM formulation. Shape model flexibility is increased, for tubular objects, by modeling the axis deformation independent of the cross-sectional deformation, and by adding supplementary cylindrical deformation modes. Furthermore, a novel appearance modeling scheme that effectively deals with a highly varying background is developed. In contrast with the conventional ASM approach, the new appearance model is trained on both boundary and non-boundary points, and the probability that a given point belongs to the boundary is estimated non-parametrically. The methods are evaluated on the complex task of segmenting thrombus in abdominal aortic aneurysms (AAA). Shape approximation errors were successfully reduced using the two shape model extensions. Segmentation using the new appearance model significantly outperformed the original ASM scheme; average volume errors are 5.1% and 45% respectively.
主动形状模型（ASM）已被证明是一种有效的图像分割方法。然而，在某些应用中，ASM中使用的围绕轮廓的灰度外观线性模型不足以进行精确的边界定位。此外，如果训练集有限，统计形状模型可能会受到过度限制。本文描述了对原始ASM公式的形状和外观模型的修改。对于管状物体，通过独立于横截面变形对轴变形进行建模，并添加补充圆柱变形模式，增加了形状模型的灵活性。此外，还开发了一种有效处理高度变化背景的新型外观建模方案。与传统的ASM方法相比，新的外观模型在边界点和非边界点上都进行了训练，并且非参数估计给定的点属于边界的概率。这些方法在腹主动脉瘤（AAA）中血栓分割的复杂任务上进行了评估。使用两种形状模型扩展成功降低了形状近似误差。使用新外观模型进行的分割明显优于原始ASM方案；平均体积误差分别为5.1%和45%。
