Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy
Detection of red lesions in color retinal images is a critical step to prevent the development of vision loss and blindness associated with diabetic retinopathy (DR).
Microaneurysms (MAs) are the most frequently observed and are usually the first lesions to appear as a consequence of DR. Therefore, their detection is necessary for mass screening of DR.
However, detecting these lesions is a challenging task because of the low image contrast, and the wide variation of imaging conditions.
In this paper we focus on developing unsupervised and supervised techniques to cope intelligently with the MAs detection problem, said Dr. Jamshid Dehmeshki, CTO of IAG, Image Analysis Group.
- In the first step, the retinal images are preprocessed to remove background variation in order to achieve a high level of accuracy in the detection.
- In the main processing step, important landmarks such as the optic nerve head and retinal vessels are detected and masked using the Radon transform (RT) and multi-overlapping windows.
- Finally, the MAs are detected and numbered by using a combination of RT and a supervised support vector machine classifier.
The method was tested on three publicly available datasets and a local database comprising a total of 749 images.
DR was detected with a sensitivity of 100% and a specificity of 93% on average across all of these databases. Moreover, from lesion-based analysis the proposed approach detected the MAs with sensitivity of 97.7% with an average of 7 false positives per image.
Read more about the detection performance and FROC analysis or reach out to talk to our experts, imaging.experts@localhost
Title: Automated Microaneurysms Detection in Retinal Images Using Radon Transform and Supervised Learning: Application to Mass Screening of Diabetic Retinopathy
Journal: IEEE Access
Authors: MEYSAM TAVAKOLI, ALIREZA MEHDIZADEH, AFSHIN AGHAYAN, REZA POURREZA SHAHRI, TIM ELLIS, AND JAMSHID DEHMESHKI
Access Online: https://ieeexplore.ieee.org/abstract/document/9409109
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