Lucas Blake C, Kazhdan Michael, Taylor Russell H
Johns Hopkins Applied Physics Laboratory, Laurel, MD, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):404-12. doi: 10.1007/978-3-642-33418-4_50.
An emerging topic is to build image segmentation systems that can segment hundreds to thousands of objects (i.e. cell segmentation\tracking, full brain parcellation, full body segmentation, etc.). Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel precision. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel precision. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label mask image and unsigned distance field. The time complexity of the algorithm is shown to be O((M (power)d)/P) for M (power)d pixels and P processing units in dimension d = {2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems.
一个新兴的课题是构建能够分割数百到数千个对象的图像分割系统（即细胞分割/跟踪、全脑分区、全身分割等）。多对象水平集方法（MLSM）以亚像素精度的优势执行此任务。然而，MLSM的当前实现不像缺乏亚像素精度的区域生长和图割方法那样在计算或内存效率方面表现出色。为了解决这一性能差距，我们提出了一种新颖的MLSM并行实现方法，该方法利用算法的稀疏特性来最小化其对多个对象的内存占用。新方法，即多对象测地线活动轮廓（MOGAC），仅用两个函数就能表示N个对象：一个标签掩码图像和无符号距离场。对于维度d = {2,3} 中的M的d次方个像素和P个处理单元，该算法的时间复杂度显示为O((M的d次方)/P)，与对象数量无关。给出了二维和三维图像分割问题的结果。
