Palomera-Pérez Miguel A, Martinez-Perez M Elena, Benítez-Pérez Hector, Ortega-Arjona Jorge Luis
Department of Computer Systems Engineering and Automatization, Instituto de Investigaciones en Matematicas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico 01600, Mexico.
IEEE Trans Inf Technol Biomed. 2010 Mar;14(2):500-6. doi: 10.1109/TITB.2009.2036604. Epub 2009 Dec 11.
This paper presents a parallel implementation based on insight segmentation and registration toolkit for a multiscale feature extraction and region growing algorithm, applied to retinal blood vessels segmentation. This implementation is capable of achieving an accuracy (Ac) comparable to its serial counterpart (about 92%), but 8 to 10 times faster. In this paper, the Ac of this parallel implementation is evaluated by comparison with expert manual segmentation (obtained from public databases). On the other hand, its performance is compared with previous published serial implementations. Both these characteristics make this parallel implementation feasible for the analysis of a larger amount of high-resolution retinal images, achieving a faster and high-quality segmentation of retinal blood vessels.
本文提出了一种基于洞察分割与配准工具包的并行实现方法，用于多尺度特征提取和区域生长算法，并将其应用于视网膜血管分割。该实现能够达到与串行实现相当的准确率（Ac）（约92%），但速度快8到10倍。本文通过与专家手动分割（从公共数据库获得）进行比较，对这种并行实现的Ac进行了评估。另一方面，将其性能与先前发表的串行实现进行了比较。这两个特性使得这种并行实现对于分析大量高分辨率视网膜图像是可行的，能够实现更快且高质量的视网膜血管分割。
