Dizdaroğlu Bekir, Ataer-Cansizoglu Esra, Kalpathy-Cramer Jayashree, Keck Katie, Chiang Michael F, Erdogmus Deniz
Computer Engineering Department, Karadeniz Technical University, Turkey ; Cognitive Systems Laboratory, Northeastern University, Boston MA, USA.
Cognitive Systems Laboratory, Northeastern University, Boston MA, USA.
IEEE Int Workshop Mach Learn Signal Process. 2012:1-6. doi: 10.1109/MLSP.2012.6349730.
In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.
在本文中，我们提出了一种基于水平集的自动视网膜血管分割方法的新颖改进。该方法引入了用于对血管中心线进行采样的脊样本提取以及用于精确区域边界检测的基于相位图的边缘检测。对于采用经典边缘检测方法的水平集方法而言，在眼底图像中分割血管通常具有挑战性。此外，通过使用脊识别对血管中心线进行采样来确定种子点进行初始化，使得该方法完全自动化。所得算法能够准确且自动地分割眼底图像中的血管。定量结果辅以可视化结果支持了这一观察。该方法可应用于医学图像分析中遇到的更广泛的血管分割问题类别。
