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首页> 外文期刊>Computer methods in biomechanics and bio >Fast optic disc segmentation using FFT-based template-matching and | region-growing techniques
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Fast optic disc segmentation using FFT-based template-matching and | region-growing techniques

机译:使用基于FFT的模板匹配和| |快速光盘分割区域生长技术

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摘要

The analysis of retinal features, such as blood vessels, optic disc and fovea, plays an important role in the detection of several diseases. This paper presents a method for automated optic disc segmentation from colour fundus images. The proposed method comprises three major stages, namely optic disc localisation, preprocessing and segmentation. Localisation is performed using the fast Fourier transform-based template matching to obtain a seed point located on the optic disc which is then used as an input to the region growing technique for the purpose of segmentation. Three sets of fundus images, namely DRIVE, MESSIDOR and a LOCAL database are used to measure the accuracy of the proposed method. From the experimental results, it is found that the proposed localisation method achieves success rates of 100, 98.91 and 97.56% for these databases, respectively, which are comparable to other known methods. The proposed segmentation method is compared with several known segmentation methods using DRIVE database. Based on the results, it is found that the proposed method achieves values of 87.16,91.27,99.81, 90.56, 98.68, and 89.71% in terms of overlap, sensitivity, specificity, positive predictive value, accuracy, and kappa coefficient respectively, which are higher compared to the results achieved by other known methods. Furthermore, the average processing time required for the optic disc localisation is 0.22 s, while the average processing time required for the entire three stages is1.03 s.
机译:视网膜特征(例如血管,视盘和中央凹)的分析在检测多种疾病中起着重要作用。本文提出了一种从彩色眼底图像自动分割视盘的方法。所提出的方法包括三个主要阶段,即光盘定位,预处理和分段。使用基于快速傅立叶变换的模板匹配执行定位,以获得位于光盘上的种子点,然后将其用作区域增长技术的输入,以进行分割。使用三组眼底图像,即DRIVE,MESSIDOR和LOCAL数据库来测量该方法的准确性。从实验结果中发现,所提出的本地化方法对于这些数据库的成功率分别为100、98.91和97.56%,与其他已知方法相当。将所提出的分割方法与使用DRIVE数据库的几种已知分割方法进行了比较。根据结果​​,发现该方法在重叠,灵敏度,特异性,阳性预测值,准确性和kappa系数方面分别达到了87.16、91.27、99.81、90.56、98.68和89.71%的值,分别为与通过其他已知方法获得的结果相比更高。此外,光盘定位所需的平均处理时间为0.22 s,而整个三个阶段所需的平均处理时间为1.03 s。

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