Computer-Aided Diagnosis systems are required to extract suitable information about retina and its changes. In particular, identifying objects of interest such as lesions and anatomical structures from the retinal images is a challenging and iterative process that is doable by image processing approaches. Microaneurysm (MAs) are one set of these changes that caused by diabetic retinopathy (DR). In fact, MAs detection is the main step for identification of DR in the retinal images analysis. The objective of this study is to apply an automated method for detection of MAs and compare the results of detection with and without vessel segmentation and masking either in the normal or abnormal image. The steps for the detection and segmentation are as follows. At the first step, we did preprocessing, by using top-hat transformation. Our main processing was included applying Radon transform, to segment the vessels and masking them. At last, we did MAs detection step using combination of Laplacian-of-Gaussian and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compare the output of our proposed method with the ground truth that collected by ophthalmologists. With vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs with 11 false positive rate per image for 100 color images in a local retinal database and 20 images of a public dataset (DRIVE). Also without vessel segmentation, our automated algorithm finds sensitivity of about 90% in detection of MAs with 73 false positives per image for all 120 images of two databases. In conclusion, with vessel segmentation we have acceptable sensitivity and specificity, as a necessary step in some diagnostic algorithm for retinal pathology.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.