Proceedings ArticleDOI
Retinal blood vessel segmentation using graph cut analysis
P. R. Wankhede,K. B. Khanchandani +1 more
- pp 1429-1432
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TLDR
A novel method for automatic segmentation of blood vessels using graph cut method that significantly enhance retinal image while suppress the noise and non-vessel structures keeping vessel information and hence detects blood vessels accurately is presented.Abstract:
The automated segmentation of blood vessels helps the ophthalmologist for early detection and possible treatment of retinal diseases. This paper presents a novel method for automatic segmentation of blood vessels using graph cut method. Initially, we applied mean filter, convolution by Gaussian kernel, shade correction and top-hat transformation as preprocessing steps for enhancement of blood vessels. It significantly enhance retinal image while suppress the noise and non-vessel structures keeping vessel information. Then vascular structure is extracted using graph cut segmentation. The proposed approach is tested on publicly available DRIVE dataset. Performance analysis is carried out and compared with other methods. The values achieved with our novel method for area under curve, accuracy, sensitivity and specificity are 0.9605, 0.9626, 0.7261 and 0.9806 respectively. This performance parameter comparison shows the effectiveness of our method for improving the segmentation results and hence detects blood vessels accurately.read more
Citations
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Journal ArticleDOI
Blood vessel extraction and optic disc removal using curvelet transform and kernel fuzzy c-means
Sudeshna Sil Kar,Santi P. Maity +1 more
TL;DR: This paper proposes an automatic blood vessel extraction method on retinal images using matched filtering in an integrated system design platform that involves curvelet transform and kernel based fuzzy c-means and demonstrates that the proposed method is very much efficient in detecting the long and the thick as well as the short and the thin vessels.
Proceedings ArticleDOI
Retinal blood vessel segmentation using matched filter and Laplacian of Gaussian
TL;DR: Laplacian of Gaussian filters are applied to fundus retinal images to detect vessels which are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) method to avoid false detection in the vessel segmentation process.
Journal ArticleDOI
Retinal blood vessel extraction using tunable bandpass filter and fuzzy conditional entropy
Sudeshna Sil Kar,Santi P. Maity +1 more
TL;DR: The combination of curvelet transform and tunable bandpass filter is found to be very much effective in edge enhancement whereas fuzzy conditional entropy efficiently distinguishes vessels of different widths.
Journal ArticleDOI
Balancing the data term of graph-cuts algorithm to improve segmentation of hepatic vascular structures
TL;DR: The proposed method improved the segmentation of small vessels in the presence of noise and compared with state-of-the-art vessel segmentation methods.
Journal ArticleDOI
Automatic Retinal Image Registration Using Blood Vessel Segmentation and SIFT Feature
TL;DR: A new retinal image registration method is proposed based on the combination of blood vessel segmentation and scale invariant feature transform (SIFT) feature, which presents competitive performance compare to other existing segmentation methods.
References
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Graph Cuts and Efficient N-D Image Segmentation
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TL;DR: This application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems.
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Detection of blood vessels in retinal images using two-dimensional matched filters
TL;DR: The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images and the results are compared to those obtained with other methods.
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