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

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 al...

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Published in:Medical & Biological Engineering & Computing Vol. 54; no. 2/3; pp. 453 - 462
Main Authors: Rahebi, Javad, Hardalaç, Fırat
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Mar2016
Online Access:View this record in EBSCOhost
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      dt: Mar2016
      vid: 54
      iid: 2/3
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      pub: Springer Nature
      place: New York, New York
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        atl: A new approach to optic disc detection in human retinal images using the firefly algorithm.
      aug:
        au:
          Rahebi, Javad
          Hardalaç, Fırat
          Hardalaç, Fırat
        affil: Department of Electrical and Electronics Engineering, Gazi University, Ankara Turkey
      sug:
        subj:
          Insects
          Optic Nerve Anatomy and Histology
          Algorithms
          Image Interpretation, Computer Assisted
          Animal Studies
          Time Factors
          Human
          Databases
          Validation Studies
          Comparative Studies
          Evaluation Research
          Multicenter Studies
      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
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        Journal Article
      ougenre: Article
    language: English
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