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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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(a) Colored retinal image (b) Gray Scale Retinal Image. (c) Green channel of the colored retinal image.
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Related In: Results  -  Collection


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fig1: (a) Colored retinal image (b) Gray Scale Retinal Image. (c) Green channel of the colored retinal image.

Mentions: (a) CLAHE: CLAHE algorithm is used for partitioning the image into contextual regions and it applies the histogram equalization to each one. Figure 1 shows the colored, the gray scale, and the green channel of the retinal fundus image. CLAHE computes the local histogram at each pixel of the retinal image and performs histogram clipping, histogram renormalization, and output pixel mapping to an intensity proportional to its rank within the histogram. Given that hi is the histogram bin and (m × m) is the contextual region, the rank rp for a pixel with intensity p is computed as follows:(1)rp=∑k=0Nmax⁡(0,hk−β)m×m‍∑i=0pmin⁡(β,hi)+p+1hhhh×∑k=0Nmax⁡(0,hk−β)m×m‍ ×m×m−1,where the clip limit β determines the contrast enhancement limit and ∑i=0pmin⁡(β, hi) describes the rank in a clipped histogram. Since each region will have a different number of clipped pixels, it is, however, beneficial to redistribute the part of the histogram that exceeds the clip limit β evenly among all histogram bins to normalize the ranks computed in different regions. This normalization is provided by ∑j=0p((∑k=0Nmax⁡⁡(0, hk − β))/(m × m)), where hk is the histogram bin in the different region. The rank of intensity iin at (x, y) is computed and scaled to produce a fractional rank r, such that 0.0 ≤ r ≤ 1.0.


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

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

(a) Colored retinal image (b) Gray Scale Retinal Image. (c) Green channel of the colored retinal image.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: (a) Colored retinal image (b) Gray Scale Retinal Image. (c) Green channel of the colored retinal image.
Mentions: (a) CLAHE: CLAHE algorithm is used for partitioning the image into contextual regions and it applies the histogram equalization to each one. Figure 1 shows the colored, the gray scale, and the green channel of the retinal fundus image. CLAHE computes the local histogram at each pixel of the retinal image and performs histogram clipping, histogram renormalization, and output pixel mapping to an intensity proportional to its rank within the histogram. Given that hi is the histogram bin and (m × m) is the contextual region, the rank rp for a pixel with intensity p is computed as follows:(1)rp=∑k=0Nmax⁡(0,hk−β)m×m‍∑i=0pmin⁡(β,hi)+p+1hhhh×∑k=0Nmax⁡(0,hk−β)m×m‍ ×m×m−1,where the clip limit β determines the contrast enhancement limit and ∑i=0pmin⁡(β, hi) describes the rank in a clipped histogram. Since each region will have a different number of clipped pixels, it is, however, beneficial to redistribute the part of the histogram that exceeds the clip limit β evenly among all histogram bins to normalize the ranks computed in different regions. This normalization is provided by ∑j=0p((∑k=0Nmax⁡⁡(0, hk − β))/(m × m)), where hk is the histogram bin in the different region. The rank of intensity iin at (x, y) is computed and scaled to produce a fractional rank r, such that 0.0 ≤ r ≤ 1.0.

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