A novel optic disc detection scheme on retinal images

Hsiao, H. K.; Liu, Chen-Chung; Yu, Chun-Yuan; Kuo, Shiau-Wei; Yu, Shyr-Shen

doi:10.1016/j.eswa.2012.02.157

Cited by 58 publications

(35 citation statements)

References 20 publications

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“…The results achieved by the application of the proposed method to the STARE and DiaRetDB1 datasets are as good as or better than those described in previous works [10,20,21,24,28]. Due to the importance of using real images, we intend to develop an authoritative system and clinical applications.…”

Section: Discussionsupporting

confidence: 55%

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

Rahebi

Hardalaç

2015

Med Biol Eng Comput

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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.

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Section: Discussionsupporting

confidence: 55%

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

Rahebi

Hardalaç

2015

Med Biol Eng Comput

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“…The quantitative comparison shows a close correlation between the automatic and manual location as well as a high spatial overlap between the OD generated by the manual method, other OD localization methodologies available in literature (Hsiao, Liu, Yu, Kuo, & Yu, 2012) and the proposed method.…”

Section: Export Of Quantitative Datamentioning

confidence: 94%

QUARTZ: Quantitative Analysis of Retinal Vessel Topology and size – An automated system for quantification of retinal vessels morphology

Fraz

Welikala

Rudnicka

et al. 2015

Expert Systems with Applications

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. . QUARTZ performs well in terms of segmentation accuracy, calibre measurement, optic disc and arteriole/venule recognition. The system provides a rich quantification of retinal vessel morphology, which has potential medical applications in identifying those at high risk, so that prophylactic measure can be initiated before onset of overt disease.

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“…There are several works on the automatic segmentation of OD in retinal images which can mainly be grouped into four categories, namely template-based methods [4,5,6,7], deformable model methods [8,9,10,11,12,13], morphological-based approaches [14,15,16], and pixel classification methods [17,18]. Within the first category, Aquino et al [4] follow a voting-type algorithm to locate a pixel within the OD as initial information to define a starting sub-image.…”

Section: Introductionmentioning

confidence: 99%

“…Then a fast and hybrid level set segmentation method with optimized parameters is used for extracting the OD boundary. In the method proposed by Hsiao et al [13], the Canny edge detector and the Hough transform are used for obtaining the edge map.…”

Section: Introductionmentioning

confidence: 99%