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Comparative study of retinal vessel segmentation based on global thresholding techniques.

Mapayi T, Viriri S, Tapamo JR - Comput Math Methods Med (2015)

Bottom Line: Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results.This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images.The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.

ABSTRACT
Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

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Measures of CLAHE combined with different filters using ISODATA threshold. It describes the average sensitivity, specificity, and accuracy of the segmentation results obtained through CLAHE with ISODATA thresholding using different filters. CLAHE with guassian filters, combined with (ATC), gives the best performance of an average accuracy of 0.94997, average sensitivity of 0.67011, and average specificity of 0.97695.
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Related In: Results  -  Collection


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fig10: Measures of CLAHE combined with different filters using ISODATA threshold. It describes the average sensitivity, specificity, and accuracy of the segmentation results obtained through CLAHE with ISODATA thresholding using different filters. CLAHE with guassian filters, combined with (ATC), gives the best performance of an average accuracy of 0.94997, average sensitivity of 0.67011, and average specificity of 0.97695.

Mentions: Figure 8 describes the average sensitivities, specificities, and accuracies of the segmentation results obtained from phase congruence-based global thresholding approaches while Figures 9 and 10 show the average sensitivities, specificities, and accuracies of the segmentation results obtained from CLAHE-based global thresholding approaches on DRIVE database.


Comparative study of retinal vessel segmentation based on global thresholding techniques.

Mapayi T, Viriri S, Tapamo JR - Comput Math Methods Med (2015)

Measures of CLAHE combined with different filters using ISODATA threshold. It describes the average sensitivity, specificity, and accuracy of the segmentation results obtained through CLAHE with ISODATA thresholding using different filters. CLAHE with guassian filters, combined with (ATC), gives the best performance of an average accuracy of 0.94997, average sensitivity of 0.67011, and average specificity of 0.97695.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4352460&req=5

fig10: Measures of CLAHE combined with different filters using ISODATA threshold. It describes the average sensitivity, specificity, and accuracy of the segmentation results obtained through CLAHE with ISODATA thresholding using different filters. CLAHE with guassian filters, combined with (ATC), gives the best performance of an average accuracy of 0.94997, average sensitivity of 0.67011, and average specificity of 0.97695.
Mentions: Figure 8 describes the average sensitivities, specificities, and accuracies of the segmentation results obtained from phase congruence-based global thresholding approaches while Figures 9 and 10 show the average sensitivities, specificities, and accuracies of the segmentation results obtained from CLAHE-based global thresholding approaches on DRIVE database.

Bottom Line: Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results.This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images.The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

View Article: PubMed Central - PubMed

Affiliation: School of Mathematics, Statistics & Computer Science, University of KwaZulu-Natal, Durban 4000, South Africa.

ABSTRACT
Due to noise from uneven contrast and illumination during acquisition process of retinal fundus images, the use of efficient preprocessing techniques is highly desirable to produce good retinal vessel segmentation results. This paper develops and compares the performance of different vessel segmentation techniques based on global thresholding using phase congruency and contrast limited adaptive histogram equalization (CLAHE) for the preprocessing of the retinal images. The results obtained show that the combination of preprocessing technique, global thresholding, and postprocessing techniques must be carefully chosen to achieve a good segmentation performance.

Show MeSH