Computer Eng. Dept, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
Software Eng. Dept, Engineering and Natural Science Faculty, Bandırma Onyedi Eylül University, Balıkesir, Turkey.
J Digit Imaging. 2022 Apr;35(2):302-319. doi: 10.1007/s10278-021-00566-8. Epub 2022 Jan 11.
Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.
视盘定位为检测其他视网膜成分(如黄斑、中央凹和视网膜血管)提供了重要线索。通过正确检测该区域,可以预防因年龄相关性黄斑变性和糖尿病性视网膜病变等疾病引起的突发性视力丧失。因此,该领域的计算机辅助诊断系统有所增加。在本文中,提出了一种用于检测视盘定位的自动化方法。在提出的方法中,通过人工蜂群算法将眼底图像从 RGB 颜色空间转换到新的颜色空间。在新的颜色空间中,视盘的定位比在 RGB 颜色空间中更清晰。在该方法中,创建了一个称为特征矩阵的矩阵。该矩阵是从包含视盘的图像补丁和不包含视盘的图像补丁的图像的颜色像素值获得的。然后,创建转换矩阵。该矩阵的初始值是随机确定的。这两个矩阵在人工蜂群算法中进行处理。最终,转换矩阵变得最佳,并应用于原始眼底图像。这样,图像被移动到新的颜色空间。对这些图像应用阈值处理,即可获得视盘定位。该方法在三个通用数据集上进行了测试,其成功率分别为 100%、96.37%和 94.42%。