Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, Surrey, UK. moazam.
IEEE Trans Biomed Eng. 2012 Sep;59(9):2538-48. doi: 10.1109/TBME.2012.2205687. Epub 2012 Jun 22.
This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.
本文提出了一种新的视网膜图像血管分割的有监督方法。该方法使用袋装和增强决策树的集成系统，并利用基于方向分析的梯度向量场、形态变换、线强度测量和 Gabor 滤波器响应的特征向量。特征向量编码信息以处理健康和病理视网膜图像。该方法在公共可用的 DRIVE 和 STARE 数据库上进行评估，这些数据库常用于此目的，也在一个新的公共视网膜血管参考数据集 CHASE_DB1 上进行评估，该数据集是来自英国儿童心脏与健康研究（CHASE）数据集的多民族儿童视网膜图像的一个子集。详细评估了集成系统的性能，所获得的准确性、速度、鲁棒性和简单性使该算法成为自动视网膜图像分析的合适工具。
