Optic disc segmentation: level set methods and blood vessels inpainting

Almazroa, Ahmed; Sun, Weiwei; Alodhayb, Sami; Raahemifar, Kaamran; Lakshminarayanan, Vasudevan

doi:10.1117/12.2254174

Cited by 8 publications

(10 citation statements)

References 6 publications

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“…Briefly, the optic nerve head was localized using the procedures explained by Almazroa et al [ 14 ] and Burman et al [ 15 ] and optic disc segmentation was introduced by Almazroa et al [ 11 ] based on inpainting the blood vessels and level set method. A fast digital image inpainting technique [ 16 ] was applied.…”

Section: Methodsmentioning

confidence: 99%

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An Automatic Image Processing System for Glaucoma Screening

Almazroa

Alodhayb²,

Raahemifar

et al. 2017

International Journal of Biomedical Imaging

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Horizontal and vertical cup to disc ratios are the most crucial parameters used clinically to detect glaucoma or monitor its progress and are manually evaluated from retinal fundus images of the optic nerve head. Due to the rarity of the glaucoma experts as well as the increasing in glaucoma's population, an automatically calculated horizontal and vertical cup to disc ratios (HCDR and VCDR, resp.) can be useful for glaucoma screening. We report on two algorithms to calculate the HCDR and VCDR. In the algorithms, level set and inpainting techniques were developed for segmenting the disc, while thresholding using Type-II fuzzy approach was developed for segmenting the cup. The results from the algorithms were verified using the manual markings of images from a dataset of glaucomatous images (retinal fundus images for glaucoma analysis (RIGA dataset)) by six ophthalmologists. The algorithm's accuracy for HCDR and VCDR combined was 74.2%. Only the accuracy of manual markings by one ophthalmologist was higher than the algorithm's accuracy. The algorithm's best agreement was with markings by ophthalmologist number 1 in 230 images (41.8%) of the total tested images.

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Section: Methodsmentioning

confidence: 99%

“…This paper gives the results from calculations of the horizontal and vertical cup to disc ratios using our previously introduced optic disc [ 11 ] and cup [ 12 ] algorithms. The algorithms were tested using the RIGA dataset.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Image Processing System for Glaucoma Screening

Almazroa

Alodhayb²,

Raahemifar

et al. 2017

International Journal of Biomedical Imaging

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“…This along with the optic disk (OD) image segmentation proposed by Almazroa et al10 leads to a comprehensive methodology for diagnosis of glaucoma from retinal fundus images.…”

Section: Introductionmentioning

confidence: 99%

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

Almazroa

Alodhayb²,

Raahemifar

et al. 2017

OPTH

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We introduce here a new technique for segmenting optic cup using two-dimensional fundus images. Cup segmentation is the most challenging part of image processing of the optic nerve head due to the complexity of its structure. Using the blood vessels to segment the cup is important. Here, we report on blood vessel extraction using first a top-hat transform and Otsu’s segmentation function to detect the curves in the blood vessels (kinks) which indicate the cup boundary. This was followed by an interval type-II fuzzy entropy procedure. Finally, the Hough transform was applied to approximate the cup boundary. The algorithm was evaluated on 550 fundus images from a large dataset, which contained three different sets of images, where the cup was manually marked by six ophthalmologists. On one side, the accuracy of the algorithm was tested on the three image sets independently. The final cup detection accuracy in terms of area and centroid was calculated to be 78.2% of 441 images. Finally, we compared the algorithm performance with manual markings done by the six ophthalmologists. The agreement was determined between the ophthalmologists as well as the algorithm. The best agreement was between ophthalmologists one, two and five in 398 of 550 images, while the algorithm agreed with them in 356 images.

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“…Some of the methods using grey scale images select only one colour plane of the three available in any colour image representation (RGB, HIS, etc.). Most of these articles claim that the OD can be easily discriminated from the G channel when analysing the RGB components of the image [ 9 , 23 – 33 ].…”

Section: Introductionmentioning

confidence: 99%

“…Frequently, blood vessels need to be previously inpainted to prevent an interference with the OD segmentation algorithm [ 23 , 27 , 33 ]. Likewise, there is a common trend of using basic image processing techniques such as histogram thresholding alone [ 30 ] or combined with other methods [ 23 – 25 , 31 – 34 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic CDR Estimation for Early Glaucoma Diagnosis

Fernández-Granero

Sarmiento

Morillo

et al. 2017

Journal of Healthcare Engineering

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Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L∗a∗b∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L∗a∗b∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

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Optic disc segmentation: level set methods and blood vessels inpainting

Cited by 8 publications

References 6 publications

An Automatic Image Processing System for Glaucoma Screening

An Automatic Image Processing System for Glaucoma Screening

Optic cup segmentation: type-II fuzzy thresholding approach and blood vessel extraction

Automatic CDR Estimation for Early Glaucoma Diagnosis

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