Effect of two different preprocessing steps in detection of optic nerve head in fundus images

Meysam Tavakoli; Mahdieh Nazar; Alireza Mehdizadeh

doi:10.1117/12.2254841

3 March 2017 Effect of two different preprocessing steps in detection of optic nerve head in fundus images

Meysam Tavakoli, Mahdieh Nazar, Alireza Mehdizadeh

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343A (2017) https://doi.org/10.1117/12.2254841
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Identification of optic nerve head (ONH) is necessary in retinal image analysis to locate anatomical components such as fovea and retinal vessels in fundus images. In this study, we first worked on two different methods for preprocessing of images after that our main method was proposed for ONH detection in color fundus images. In the first preprocessing method, we did color space conversion, illumination equalization, and contrast enhancement and separately in the second method we applied top-hat transformation to an image. In the next step, Radon transform is applied to each of these two preprocessed fundus image to find candidates for the location of the ONH. Then, the accurate location was found using the minimum mean square error estimation. The accuracy of this method was approved by the results. Our method detected ONH correctly in 110 out of 120 images in our local database and 38 out of 40 color images in the DRIVE database by using Illumination equalization and contrast enhancement preprocessing. Moreover, by use of top-hat transformation our approach correctly detected the ONHs in 106 out of 120 images in the local database and 36 out of 40 images in the DRIVE set. In addition, Sensitivity and specificity of pixel base analysis of this algorithm seems to be acceptable in comparison with other methods.

Citation Download Citation

Meysam Tavakoli, Mahdieh Nazar, and Alireza Mehdizadeh "Effect of two different preprocessing steps in detection of optic nerve head in fundus images", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343A (3 March 2017); https://doi.org/10.1117/12.2254841

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