Segmentation of retinal blood vessels by top hat multi-scale detection for optic disc removal

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a preprocessing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the preprocessing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.

Original languageEnglish
Pages (from-to)47-53
Number of pages7
JournalJurnal Teknologi
Volume77
Issue number6
DOIs
Publication statusPublished - 2015

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Blood vessels
Optics
Screening
Processing

Keywords

  • Multi-scale line detection
  • Retinal image
  • Segmentation

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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title = "Segmentation of retinal blood vessels by top hat multi-scale detection for optic disc removal",
abstract = "Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a preprocessing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the preprocessing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.",
keywords = "Multi-scale line detection, Retinal image, Segmentation",
author = "Ain Nazari and Mustafa, {Mohd. Marzuki} and Zulkifley, {Mohd Asyraf}",
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N2 - Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a preprocessing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the preprocessing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.

AB - Nowadays, an automatic retinal vessels segmentation is important component in computer assisted system to detect numerous eye abnormalities. There are various sizes of the retinal blood vessels captured from fundus image modality, which can be detected by using multi-scale approach. However, the main limitation of the current multi-scale approaches is the inability to remove the optic disc from the detected blood vessels. In this paper, a hybrid of multi-scale detection with pre-processing approach is proposed so that clearer vessel segmentation can be obtained. The proposed method embedded with a preprocessing phase that includes four series of processes that include Top-hat transformation as the main part. This technique will reduce the influence of the structure of optic disc and enhance the contrast of the vessel from the background. Then, the result from the preprocessing phase will be fed to the multi-scale detection to perform the segmentation. The proposed method is evaluated on two publicly available online databases: HRF and DRIVE. On HRF database, the best obtained precision and specificity values are 0.9689 and 0.9989, respectively. Meanwhile, for DRIVE database, the system performs well in all performance measures: precision, specificity, accuracy and error with the best values of 0.7541, 0.9739, 0.9510 and 0.0490, respectively. In conclusion, the proposed method is able to filter the unwanted optical disc from the fundus image effectively. Thus, retinal blood vessel image can be used for further analysis process and beneficial for pre-screening system development.

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