Automatic Segmentation for Retinal Vessel Based on Multi-scale 2D Gabor Wavelet
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摘要: 眼底视网膜血管分割对临床视网膜疾病诊断具有重要意义. 由于视网膜血管结构微小, 血管轮廓边界模糊, 加上图像采集时噪声的影响, 视网膜血管分割非常困难. 本文提出一种视网膜血管自动分割新方法. 首先, 应用对比度受限的自适应直方图均衡法增强视网膜图像;然后, 采用不同尺度的2D Gabor小波对视网膜图像进行变换, 并分别应用形态学重构 (Morphological reconstruction, MR)和区域生长法 (Region growing, RG)对变换后的图像进行分割; 最后, 对以上两种方法分割的视网膜血管和背景像素点重新标记识别, 得到视网膜血管最终分割结果. 通过对DRIVE和STARE数据库视网膜图像的分割实验, 证明了该算法的有效性.
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关键词:
- 视网膜血管分割 /
- 2D Gabor小波 /
- 形态学重构 /
- 区域生长
Abstract: Segmentation of retinal vessels plays an important role in the diagnostic procedure of retinopathy. Due to the fact that the retinal vessels usually have some tiny structures and blurred boundaries, especially with remarkable noises resulted from retinal image acquisition, it is difficult to segment vessels from retinal images. In this paper, a new automatic segmentation method for retinal vessels is proposed. Firstly, the retinal vessel image is enhanced by the contrast-limited adaptive histogram equalization, and followed by multi-scale 2D Gabor wavelet transformation. Then, the use morphological reconstruction (MR) and region growing (RG) are used respectively to extract retinal vessels. Finally, both the segmented results are combined to achieve the final segmentation by reclassifying the vessel and background pixels. Experiments are conducted on the publicly available DRIVE and STARE databases, which show the effectiveness of the proposed method on retinal vessel segmentation. -
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