Xu Juan, Chutatape Opas, Chew Paul
Department of Ophthalmology, School of Medicine, University of Pittsburgh, 203 Lothrop Street, EEI-834, Pittsburgh, PA 15213, USA.
IEEE Trans Biomed Eng. 2007 Mar;54(3):473-82. doi: 10.1109/TBME.2006.888831.
This paper presents a novel deformable-model-based algorithm for fully automated detection of optic disk boundary in fundus images. The proposed method improves and extends the original snake (deforming-only technique) in two aspects: clustering and smoothing update. The contour points are first self-separated into edge-point group or uncertain-point group by clustering after each deformation, and these contour points are then updated by different criteria based on different groups. The updating process combines both the local and global information of the contour to achieve the balance of contour stability and accuracy. The modifications make the proposed algorithm more accurate and robust to blood vessel occlusions, noises, ill-defined edges and fuzzy contour shapes. The comparative results show that the proposed method can estimate the disk boundaries of 100 test images closer to the groundtruth, as measured by mean distance to closest point (MDCP) <3 pixels, with the better success rate when compared to those obtained by gradient vector flow snake (GVF-snake) and modified active shape models (ASM).
本文提出了一种基于可变形模型的全新算法，用于眼底图像中视盘边界的全自动检测。所提出的方法在两个方面改进并扩展了原始的蛇形算法（仅变形技术）：聚类和平滑更新。每次变形后，轮廓点首先通过聚类自动分离为边缘点组或不确定点组，然后根据不同的组采用不同的准则对这些轮廓点进行更新。更新过程结合了轮廓的局部和全局信息，以实现轮廓稳定性和准确性的平衡。这些修改使得所提出的算法对于血管阻塞、噪声、边缘不清晰和轮廓形状模糊更加准确和鲁棒。比较结果表明，所提出的方法能够估计100张测试图像的视盘边界，与真实边界更接近，通过到最近点的平均距离（MDCP）<3像素来衡量，与梯度向量流蛇形算法（GVF - snake）和改进的主动形状模型（ASM）相比，成功率更高。
