人工智能
尺度不变特征变换
计算机视觉
计算机科学
像素
模式识别(心理学)
沃罗诺图
图像配准
特征提取
数学
图像(数学)
几何学
作者
Jalil Jalili,Marjaneh Hejazi,Mohammad Riazi‐Esfahani,Arash Eliasi,Mohsen Ebrahimi,Mojtaba Seydi,Masoud Aghsaei Fard,Alireza Ahmadian
出处
期刊:Journal of medical imaging
[SPIE - International Society for Optical Engineering]
日期:2020-07-15
卷期号:7 (04): 1-1
标识
DOI:10.1117/1.jmi.7.4.044001
摘要
Purpose: Peripheral retinal lesions substantially increase the risk of diabetic retinopathy and retinopathy of prematurity. The peripheral changes can be visualized in wide field imaging, which is obtained by combining multiple images with an overlapping field of view using mosaicking methods. However, a robust and accurate registration of mosaicking techniques for normal angle fundus cameras is still a challenge due to the random selection of matching points and execution time. We propose a method of retinal image mosaicking based on scale-invariant feature transformation (SIFT) feature descriptor and Voronoi diagram. Approach: In our method, the SIFT algorithm is used to describe local features in the input images. Then the input images are subdivided into regions based on the Voronoi method. Each pair of Voronoi regions is matched by the method zero mean normalized cross correlation. After matching, the retinal images are mapped into the same coordinate system to form a mosaic image. The success rate and the mean registration error (RE) of our method were compared with those of other state-of-the-art methods for the P category of the fundus image registration database. Results: Experimental results show that the proposed method accurately registered 42% of retinal image pairs with a mean RE of 3.040 pixels, while a lower success rate was observed in the other four state-of-the-art retinal image registration methods GDB-ICP (33%), Harris-PIIFD (0%), HM-2016 (0%), and HM-2017 (2%). Conclusions: The proposed method outperforms state-of-the-art methods in terms of quality and running time and reduces the computational complexity.
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