Analyzing the effect of multi-channel multi-scale segmentation of retinal blood vessels

Research output: Contribution to journalArticle

Abstract

Retinal blood vessel segmentation is one of the important modules in developing an automated vessel detection system, which is used to pre-screen various types of disease. This paper proposes a segmentation technique for retinal blood vessel using multi-channel multi-scale edge detection method. Multi-channel approach is implemented because of more information can be extracted compared to a single channel. After that, multi-scale edge detection is applied to detect the blood vessels which vary in size from 1 pixel to 15 pixels width. The contrast is then improved through standardization of the original response image. Finally, binarization method is applied to remove the noise to get the final segmented retinal blood vessels. Simulation results show that blood vessels have been segmented accurately by using images from two publicly available databases, DRIVE and HRF. The best accuracy is 0.93 obtained from DRIVE database while the finest precision is 0.94 obtained from HRF database. Meanwhile, the highest sensitivity obtained is 0.61 from DRIVE database whereas the best specificity is 0.98 based on HRF database. In conclusion, an accurate information of retinal blood vessel condition will be very beneficial to pre-screen numerous diseases.

Original languageEnglish
Pages (from-to)8358-8362
Number of pages5
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number18
Publication statusPublished - 2015

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Blood vessels
Edge detection
Pixels
Standardization

Keywords

  • Multi-channel
  • Multi-scale
  • Retinal image
  • Segmentation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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title = "Analyzing the effect of multi-channel multi-scale segmentation of retinal blood vessels",
abstract = "Retinal blood vessel segmentation is one of the important modules in developing an automated vessel detection system, which is used to pre-screen various types of disease. This paper proposes a segmentation technique for retinal blood vessel using multi-channel multi-scale edge detection method. Multi-channel approach is implemented because of more information can be extracted compared to a single channel. After that, multi-scale edge detection is applied to detect the blood vessels which vary in size from 1 pixel to 15 pixels width. The contrast is then improved through standardization of the original response image. Finally, binarization method is applied to remove the noise to get the final segmented retinal blood vessels. Simulation results show that blood vessels have been segmented accurately by using images from two publicly available databases, DRIVE and HRF. The best accuracy is 0.93 obtained from DRIVE database while the finest precision is 0.94 obtained from HRF database. Meanwhile, the highest sensitivity obtained is 0.61 from DRIVE database whereas the best specificity is 0.98 based on HRF database. In conclusion, an accurate information of retinal blood vessel condition will be very beneficial to pre-screen numerous diseases.",
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author = "Ain Nazari and Mustafa, {Mohd. Marzuki} and Zulkifley, {Mohd Asyraf}",
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AU - Mustafa, Mohd. Marzuki

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N2 - Retinal blood vessel segmentation is one of the important modules in developing an automated vessel detection system, which is used to pre-screen various types of disease. This paper proposes a segmentation technique for retinal blood vessel using multi-channel multi-scale edge detection method. Multi-channel approach is implemented because of more information can be extracted compared to a single channel. After that, multi-scale edge detection is applied to detect the blood vessels which vary in size from 1 pixel to 15 pixels width. The contrast is then improved through standardization of the original response image. Finally, binarization method is applied to remove the noise to get the final segmented retinal blood vessels. Simulation results show that blood vessels have been segmented accurately by using images from two publicly available databases, DRIVE and HRF. The best accuracy is 0.93 obtained from DRIVE database while the finest precision is 0.94 obtained from HRF database. Meanwhile, the highest sensitivity obtained is 0.61 from DRIVE database whereas the best specificity is 0.98 based on HRF database. In conclusion, an accurate information of retinal blood vessel condition will be very beneficial to pre-screen numerous diseases.

AB - Retinal blood vessel segmentation is one of the important modules in developing an automated vessel detection system, which is used to pre-screen various types of disease. This paper proposes a segmentation technique for retinal blood vessel using multi-channel multi-scale edge detection method. Multi-channel approach is implemented because of more information can be extracted compared to a single channel. After that, multi-scale edge detection is applied to detect the blood vessels which vary in size from 1 pixel to 15 pixels width. The contrast is then improved through standardization of the original response image. Finally, binarization method is applied to remove the noise to get the final segmented retinal blood vessels. Simulation results show that blood vessels have been segmented accurately by using images from two publicly available databases, DRIVE and HRF. The best accuracy is 0.93 obtained from DRIVE database while the finest precision is 0.94 obtained from HRF database. Meanwhile, the highest sensitivity obtained is 0.61 from DRIVE database whereas the best specificity is 0.98 based on HRF database. In conclusion, an accurate information of retinal blood vessel condition will be very beneficial to pre-screen numerous diseases.

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