Abstract
All around the world, partial or total blindness has become a direct consequence of diabetes and hypertension. Visual disorders related to these diseases require automatic and specialized methods to detect early malformations, artifacts, or irregular structures for helping specialists in the diagnosis. This study presents an innovative methodology for detecting and evaluating retinopathies, particularly microaneurysm and hemorrhages. The method is based on a multidirectional Fractional-Order Gaussian Filters tuned by the Differential Evolution algorithm. The contrast of the microaneurysms and hemorrhages, regarding the background, is improved substantially. After that, these structures are extracted using the Kittler thresholding method under additional considerations. Then, candidate lesions are detected by removing the blood vessels and fovea pixels in the resulting image. Finally, candidate lesions are classified according to its size, shape, and intensity properties via Support Vector Machines with a radial basis function kernel. The proposed method is evaluated by using the publicly available database MESSIDOR for detecting microaneurysms. The numerical results are summarized by the averaged binary metrics of accuracy, sensitivity, and specificity giving the performance values of 0.9995, 0.7820 and 0.9998, respectively.
Highlights
The stress, inappropriate feeding behaviors, lack of interest in prevention methods, besides the extensive periods without proper medical screening, have increased the spread of retinal maladies in the world
We present an innovative framework powered by a multidirectional filter based on the Fractional-Order Gaussian Function (FOGF)
An innovative framework for enhancing and detecting microaneurysms in fundus images is presented. It applies a multidirectional filter based on Fractional-Order Gaussian Filters (FOGFs)
Summary
The stress, inappropriate feeding behaviors, lack of interest in prevention methods, besides the extensive periods without proper medical screening, have increased the spread of retinal maladies in the world. To determine the degree of DR, medical specialists can use diagnosis techniques based on the analysis of length, orientation, position, and width of the structural elements in the patients’ retina These detection systems quickly analyze more volumes of information than technicians or specialists. Most retinopathies are originated by alterations in the blood flow irrigation of the retinal vascular system; such affections can be classified in diabetic, hypertension, and pigmentary retinopathies [5] Detection techniques such as segmentation, filtering, and automatic classification should be supported by specialized acquisition systems and technicians. Retinography is a noninvasive diagnostic technique that certainly does not use contrast agents in the acquisition process [17] This technique produces color images from the inner part of an eye, well-known as Fundus Images (FIs), which can be used to detect some diseases, e.g., diabetic retinopathies and glaucoma. Recent studies have shown a direct link between the retinal state and its association and potential use in the diagnosis of dementia [18], Parkinson’s [19], and Alzheimer’s diseases [20], as well as other cognitive deficits [21]
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