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Fast automatic fundus images registration and mosaic based on Compute Unified Device Architecture

机译:基于Compute Unified Device Architecture的快速眼底图像自动配准和拼接

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In order to overcome the characteristics of low contrast, non-uniform illumination, limited field of view (FOV), and the geometric distortion between different FOV of the fundus images, a fast automatic fundus image registration and mosaic algorithm based on Compute Unified Device Architecture (CUDA) is presented. Firstly fundus images are enhanced by homomorphism filtering, then the Scale Invariant Feature Transform (SIFT) features in effective FOV are extracted and matched between images with CUDA speeded up. With CUDA application, point pairs are purified using random sample consensus (RANSAC) algorithm employed perspective model, transformation matrixes are computed according to the matching point pairs of surrounding FOV images to the central, image registration and image fusion is implemented to get fundus panoramic image finally. The automatic registration and mosaic results of multiple FOV images obtained by fundus camera show that the algorithm is robust and stability with registration accuracy up to pixel level, the algorithm speed upgrade 10 to 30 times, high-precision automatic fundus image mosaic can be achieved.
机译:为了克服眼底图像对比度低,照度不均匀,视野受限(FOV)以及眼底图像不同FOV之间的几何畸变的特点,提出了一种基于Compute Unified Device Architecture的快速自动眼底图像配准和镶嵌算法。 (CUDA)。首先通过同态滤波对眼底图像进行增强,然后提取有效FOV中的尺度不变特征变换(SIFT)特征,并在加速CUDA的图像之间进行匹配。在CUDA应用中,使用透视模型的随机样本共识(RANSAC)算法对点对进行纯化,根据周围FOV图像与中心的匹配点对计算变换矩阵,并进行图像配准和图像融合以获得眼底全景图像最后。通过眼底摄像头获得的多幅FOV图像的自动配准和镶嵌结果表明,该算法鲁棒性和稳定性好,配准精度达到像素级,算法速度提高了10到30倍,可以实现高精度的自动眼底图像配准。

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