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Retinal blood vessel segmentation and bifurcation detection using combined filters

机译:使用组合过滤器进行视网膜血管分割和分叉检测

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In this research, retinal blood vessel fundus images will be segmented. Moreover, bifurcation point of the segmented blood vessel images will be determined. The data used for this research was obtained from fundus image database of DRIVE (Digital Retinal Images for Vessel Extraction) dataset. The preprocessing phases contain of several steps such as green channel extraction, histogram equalization and optic disc elimination. Meanwhile, the segmentation phase was performed by combining two filtering methods. These methods are median and derivative of Gaussian filter. Median filter was used to reduce the noise on retinal fundus image such as hard and soft exudates as well as dot and blot hemorrhages. Combination of these two filtering methods were also employed to determine the bifurcation point of segmented blood vessel image. The bifurcation points and the result from blood vessel segmentation were among two of the unique parameters of retinal fundus image. These parameters will be used eventually as unique features of the advanced research which is a biometric system to identify an individual based on his/her unique retinal pattern.
机译:在这项研究中,视网膜血管眼底图像将被分割。此外,将确定分割的血管图像的分叉点。用于这项研究的数据是从DRIVE(眼底血管提取数字视网膜图像)数据集的眼底图像数据库中获得的。预处理阶段包含几个步骤,例如绿色通道提取,直方图均衡和光盘消除。同时,通过组合两种滤波方法来执行分割阶段。这些方法是高斯滤波器的中值和导数。使用中值过滤器来减少视网膜眼底图像上的噪音,例如硬性和软性渗出物以及斑点和斑点出血。这两种过滤方法的组合也用于确定分割后的血管图像的分叉点。分叉点和血管分割的结果是视网膜眼底图像的两个独特参数之一。这些参数将最终用作高级研究的独特功能,该功能是一种生物识别系统,可以根据他/她独特的视网膜模式识别一个人。

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