A new approach to optic disc detection in human retinal images using the firefly algorithm

Abstract

There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.

Computational efficiency of optic disk detection on fundus image: a survey

“…Then, morphological operators and contrast enhancement techniques (Gamma transformations) are used in conjunction with the difference of the Gaussian filter (DOG) to obtain the OD border. In the work proposed by Rahebi et al [5], a median filter in performed to denoising the retinal fundus image. Then, the optical disk center is determined using the Firefly that moves towards a pixel of high intensity.…”

“…Furthermore, some retinal pathology such as neovascularization (DR), wet AMD, leads to provide mistaken vessel characteristics. The work described in [1,4,5,9] have proposed usingOD features such as brightness, shape and size. The work [10,6] uses vascularization information and is based on the fact that the vessels emerge from the OD.…”

“…The mainly objectives are the extractionof retina and retinal components, theenhancement of image contrast [7,4,3,1], orthe denoising [5]. It can be seen in Table 3 that all preprocessing have complexity between n² and 28.n².…”

“…It can be seen in Table 3 that all preprocessing have complexity between n² and 28.n². We deduce that preprocessing complexity is quadratic with order of O (n²).Themethods, whose location is based on the OD characteristics, proceed to detect all shapes with respect to the brightest and roundness using standard processing such as intensity threshold [9], Difference of Gaussian (DOG) filter [4] and principal component analysis [5]. These processing have quadratic processing complexity, of the order of O (n²) such as the worksdescribed in [1, 4, and 5].…”

“…(a), the OD appeared usually as a relatively circular yellowish disk having an average diameter of 1600μm [1]. The shape of the OD is similar to an ellipse with a width of 1.8 ± 0.2 mm and a length of 1.9 ± 0.2 mm [5]. ODis usually brighter than the fundus [4].…”