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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.

Show MeSH
Shows different segmentation results obtained through CLAHE with different filters using Otsu thresholding technique. Images (d1), (e1), and (f1) are DRIVE database gold standards. Images (d2), (e2), and (f2) are images segmented using Otsu threshold with Gaussian filter. Images (d3), (e3), and (f3) are images segmented using Otsu threshold with average filter. Images (d4), (e4), and (f4) are images segmented using Otsu threshold with adaptive filter. Images (d5), (e5), and (f5) are images segmented using Otsu threshold with combination of average and Gaussian filters.
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fig5: Shows different segmentation results obtained through CLAHE with different filters using Otsu thresholding technique. Images (d1), (e1), and (f1) are DRIVE database gold standards. Images (d2), (e2), and (f2) are images segmented using Otsu threshold with Gaussian filter. Images (d3), (e3), and (f3) are images segmented using Otsu threshold with average filter. Images (d4), (e4), and (f4) are images segmented using Otsu threshold with adaptive filter. Images (d5), (e5), and (f5) are images segmented using Otsu threshold with combination of average and Gaussian filters.

Mentions: (c) Filters: the resulting images from CLAHE preprocessing technique are still affected to some extent by noise. In order to further enhance the retinal images, different filters are considered. The different filters considered are adaptive filter, average filter, and Gaussian filter. The combination of average filter and Gaussian filter was also used to further enhance the output of CLAHE preprocessing technique. Each of these different filtering approaches was considered in order to investigate their suitability for further enhancement of the retinal image. In related development, the resulting images from phase congruence were also enhance using average filter. The performance of each of the filtering approaches was, however, measured after the final vessel segmentation. The visual results from DRIVE database can be seen in Figures 4, 5, and 7 and those of STARE database in Figures 11 and 12.


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

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

Shows different segmentation results obtained through CLAHE with different filters using Otsu thresholding technique. Images (d1), (e1), and (f1) are DRIVE database gold standards. Images (d2), (e2), and (f2) are images segmented using Otsu threshold with Gaussian filter. Images (d3), (e3), and (f3) are images segmented using Otsu threshold with average filter. Images (d4), (e4), and (f4) are images segmented using Otsu threshold with adaptive filter. Images (d5), (e5), and (f5) are images segmented using Otsu threshold with combination of average and Gaussian filters.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Shows different segmentation results obtained through CLAHE with different filters using Otsu thresholding technique. Images (d1), (e1), and (f1) are DRIVE database gold standards. Images (d2), (e2), and (f2) are images segmented using Otsu threshold with Gaussian filter. Images (d3), (e3), and (f3) are images segmented using Otsu threshold with average filter. Images (d4), (e4), and (f4) are images segmented using Otsu threshold with adaptive filter. Images (d5), (e5), and (f5) are images segmented using Otsu threshold with combination of average and Gaussian filters.
Mentions: (c) Filters: the resulting images from CLAHE preprocessing technique are still affected to some extent by noise. In order to further enhance the retinal images, different filters are considered. The different filters considered are adaptive filter, average filter, and Gaussian filter. The combination of average filter and Gaussian filter was also used to further enhance the output of CLAHE preprocessing technique. Each of these different filtering approaches was considered in order to investigate their suitability for further enhancement of the retinal image. In related development, the resulting images from phase congruence were also enhance using average filter. The performance of each of the filtering approaches was, however, measured after the final vessel segmentation. The visual results from DRIVE database can be seen in Figures 4, 5, and 7 and those of STARE database in Figures 11 and 12.

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