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Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-bands and Distance Regularized Level Set Evolution.

Soltanipour A, Sadri S, Rabbani H, Akhlaghi MR - J Med Signals Sens (2015 Jul-Sep)

Bottom Line: In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales.The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing.The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.

ABSTRACT
This paper presents a new procedure for automatic extraction of the blood vessels and optic disk (OD) in fundus fluorescein angiogram (FFA). In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales. For this purpose, each directional image is processed by using information of the first order derivative and eigenvalues obtained from the Hessian matrix. The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing. After extracting blood vessels from FFA image, candidates regions for OD are enhanced by removing blood vessels from the FFA image, using multi-structure elements morphology, and modification of FDCT coefficients. Then, canny edge detector and Hough transform are applied to the reconstructed image to extract the boundary of candidate regions. At the next step, the information of the main arc of the retinal vessels surrounding the OD region is used to extract the actual location of the OD. Finally, the OD boundary is detected by applying distance regularized level set evolution. The proposed method was tested on the FFA images from angiography unit of Isfahan Feiz Hospital, containing 70 FFA images from different diabetic retinopathy stages. The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

No MeSH data available.


Related in: MedlinePlus

(a) Vessels image. (b) Main arc of the vessels around optic disk region
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Figure 17: (a) Vessels image. (b) Main arc of the vessels around optic disk region

Mentions: Step 2: In this step, to determine main arc of the retinal vessels, the length of vessel candidate segments resulting from previous step is calculated by considering the number of pixels in vertical direction. Then, mean and standard value of the distribution of length of vessel candidate segments are calculated and the candidate segments with the length smaller than total mean and standard value are removed from the image. Afterward, the center of mass of each segment remaining from previous step is calculated and Euclidean distance between its center of mass and center of mass of other segments is computed. Then, pair of segments with the minimum Euclidean distances is considered and mean of the distribution of Euclidean distances of remained pairs is calculated. Finally, only pair of segments with the Euclidean distances smaller than the mean value is preserved and the others are removed from the image. So, the main arc of the retinal vessels is extracted by implementation of these two steps. Figure 17 shows the main arc of the vessels around the OD region.


Analysis of Fundus Fluorescein Angiogram Based on the Hessian Matrix of Directional Curvelet Sub-bands and Distance Regularized Level Set Evolution.

Soltanipour A, Sadri S, Rabbani H, Akhlaghi MR - J Med Signals Sens (2015 Jul-Sep)

(a) Vessels image. (b) Main arc of the vessels around optic disk region
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4528352&req=5

Figure 17: (a) Vessels image. (b) Main arc of the vessels around optic disk region
Mentions: Step 2: In this step, to determine main arc of the retinal vessels, the length of vessel candidate segments resulting from previous step is calculated by considering the number of pixels in vertical direction. Then, mean and standard value of the distribution of length of vessel candidate segments are calculated and the candidate segments with the length smaller than total mean and standard value are removed from the image. Afterward, the center of mass of each segment remaining from previous step is calculated and Euclidean distance between its center of mass and center of mass of other segments is computed. Then, pair of segments with the minimum Euclidean distances is considered and mean of the distribution of Euclidean distances of remained pairs is calculated. Finally, only pair of segments with the Euclidean distances smaller than the mean value is preserved and the others are removed from the image. So, the main arc of the retinal vessels is extracted by implementation of these two steps. Figure 17 shows the main arc of the vessels around the OD region.

Bottom Line: In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales.The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing.The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran.

ABSTRACT
This paper presents a new procedure for automatic extraction of the blood vessels and optic disk (OD) in fundus fluorescein angiogram (FFA). In order to extract blood vessel centerlines, the algorithm of vessel extraction starts with the analysis of directional images resulting from sub-bands of fast discrete curvelet transform (FDCT) in the similar directions and different scales. For this purpose, each directional image is processed by using information of the first order derivative and eigenvalues obtained from the Hessian matrix. The final vessel segmentation is obtained using a simple region growing algorithm iteratively, which merges centerline images with the contents of images resulting from modified top-hat transform followed by bit plane slicing. After extracting blood vessels from FFA image, candidates regions for OD are enhanced by removing blood vessels from the FFA image, using multi-structure elements morphology, and modification of FDCT coefficients. Then, canny edge detector and Hough transform are applied to the reconstructed image to extract the boundary of candidate regions. At the next step, the information of the main arc of the retinal vessels surrounding the OD region is used to extract the actual location of the OD. Finally, the OD boundary is detected by applying distance regularized level set evolution. The proposed method was tested on the FFA images from angiography unit of Isfahan Feiz Hospital, containing 70 FFA images from different diabetic retinopathy stages. The experimental results show the accuracy more than 93% for vessel segmentation and more than 87% for OD boundary extraction.

No MeSH data available.


Related in: MedlinePlus