解耦活动曲面用于体积图像分割

A. Mishra, P. Fieguth, David A Clausi
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引用次数: 2

摘要

寻找体积三维物体的表面是计算机视觉中的一个基本问题。能量最小化样条,如活动曲面,已被用于执行此类任务,在内部和外部能量的影响下进化,直到模型收敛到所需的曲面。目前基于可变形模型的表面提取技术计算成本高,在识别有噪声、高曲率和杂乱的三维物体表面时通常不可靠。提出了一种新的解耦活动曲面(DAS)来识别体积三维物体的表面。该算法引入了两个新的方面,从而实现了鲁棒、高效和精确的收敛。首先,DAS不是使用参数化曲面,而是使用一致的三角形网格来表示曲面,而参数化曲面会导致处理复杂形状和参数奇异性的困难。其次,受早期二维分割成功的启发,DAS分别处理两个能量分量,并使用新颖的求解技术来有效地分别最小化两个能量项。利用自然和合成体图像对静态三维物体进行分割,取得了良好的收敛效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decoupled Active Surface for Volumetric Image Segmentation
Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.
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