Optic disk segmentation is a prerequisite step in automatic retinal screening systems. In this paper, we propose an algorithm for optic disk segmentation based on a local adaptive thresholding method. Location of the optic disk is validated by intensity and average vessel width of retinal images. Then an adaptive thresholding is applied on the temporal and nasal part of the optic disc separately. Adaptive thresholding, makes our algorithm robust to illumination variations and various image acquisition conditions. Moreover, experimental results on the DRIVE and KHATAM databases show promising results compared to the recent literature. In the DRIVE database, the optic disk in all images is correctly located and the mean overlap reached to 43.21 . The optic disk is correctly detected in 98 of the images with the mean overlap of 36.32 in the KHATAM database.
@cite_3 proposed an algorithm based on curvelet transform and deformable vibrational level set model. In this algorithm probable optic disk areas are obtained by applying curvelet transform and selecting the brightest area on the enhanced images. Then the region with the high value coefficients in the modified reconstructed image is choosen as the optic disk location. Finally, blood vessels are removed by morphological operation and the optic disk boundary extracted using level set deformable model. This algorithm has two problems: first, applying curvelet transform and morphological operation to the entire image is time consuming. Also, using @cite_8 algorithm to modify the curvelet coefficient following by the constant threshold fails to detect reliable blood vessels, because of vessel-like patterns such as hemorrages and micro aneurysms. Therefore, making decision based on these results affect the final result.
@cite_3 @cite_8
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Efficient optic disk (OD) localization and segmentation are important tasks in automated retinal screening. In this paper, we take digital curvelet transform (DCUT) of the enhanced retinal image and modify its coefficients based on the sparsity of curvelet coefficients to get probable location of OD. If there are not yellowish objects in retinal images or their size are negligible, we can then directly detect OD location by performing Canny edge detector to reconstructed image with modified coefficients. Otherwise, if the size of these objects is eminent, we can see circular regions in edge map as candidate regions for OD. In this case, we use some morphological operations to fill these circular regions and erode them to get final locations for candidate regions and remove undesired pixels in edge map. Finally, we choose the candidate region that has maximum summation of pixels in strongest edge map that obtained by performing threshold to curvelet-based enhanced image, as final location of OD. This method has been tested on different retinal image datasets and quantitative results are presented. We present a new method for contrast enhancement based on the curvelet transform. The curvelet transform represents edges better than wavelets, and is therefore well-suited for multiscale edge enhancement. We compare this approach with enhancement based on the wavelet transform, and the multiscale retinex. In a range of examples, we use edge detection and segmentation, among other processing applications, to provide for quantitative comparative evaluation. Our findings are that curvelet based enhancement out-performs other enhancement methods on noisy images, but on noiseless or near noiseless images curvelet based enhancement is not remarkably better than wavelet based enhancement.