Retinal Mosaicking with Vascular Bifurcations Detected on Vessel Mask by a Convolutional Network
Retinal Mosaicking with Vascular Bifurcations Detected on Vessel Mask by a Convolutional Network
复制标题
通过卷积网络在血管掩模上检测到血管分叉的视网膜马赛克
DOI：
10.1155/2020/7156408
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发表时间：
2020-01-09
影响因子：
--
通讯作者：
Yang, Wei
中科院分区：
文献类型：
--
作者：
Feng, Xiuxia;Cai, Guangwei;Yang, Wei
视网膜图像的拼接对眼科医生和计算机辅助诊断方案有潜在的用处。血管分叉可以作为视网膜图像匹配和拼接的特征。采用全卷积网络模型对视网膜图像中的血管结构进行分割，检测血管分叉。然后，采用鲁棒高效的方法提取分岔点作为血管掩膜上的特征点；根据血管分岔的对应关系，可以估计出拼接的变换参数。对14只不同眼睛的62张视网膜图像进行了特征检测与拼接。与加速鲁棒特征和尺度不变特征变换相比，该方法实现了更高的匹配平均召回率。该方法的运行时间也比其他方法低。该方法优于AutoStitch、photoshopcs6和ICE中的photomerge功能，结果表明，对检测到的血管分叉进行精确匹配可以实现高质量的视网膜图像拼接。
Mosaicking of retinal images is potentially useful for ophthalmologists and computer-aided diagnostic schemes. Vascular bifurcations can be used as features for matching and stitching of retinal images. A fully convolutional network model is employed to segment vascular structures in retinal images to detect vascular bifurcations. Then, bifurcations are extracted as feature points on the vascular mask by a robust and efficient approach. Transformation parameters for stitching can be estimated from the correspondence of vascular bifurcations. The proposed feature detection and mosaic method is evaluated on retinal images of 14 different eyes, 62 retinal images. The proposed method achieves a considerably higher average recall rate of matching for paired images compared with speeded-up robust features and scale-invariant feature transform. The running time of our method was also lower than other methods. Results produced by the proposed method superior to that of AutoStitch, photomerge function in Photoshop cs6 and ICE, demonstrate that accurate matching of detected vascular bifurcations could lead to high-quality mosaic of retinal images.