• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.119-128
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• 2014

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.545-550
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• 2009

A method fully utilizing multiscale line filter responses is presented to estimate the point spread function(PSF) of a CT scanner and diameters of small tubular structures based on the PSF. The estimation problem is formulated as a least square fitting of a sequence of multiscale responses obtained at each medical axis point to the precomputed multiscale response curve for the ideal line model. The method was validated through phantom experiments and demonstrated through phantom experiments and demonstrated to accurately measure small-diameter structures which are significantly overestimated by conventional methods based on the full width half maximum(FWHM) and zero-crossing edge detection.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.174-180
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• 2005

Points of sharp variations in image are the most important components when we analyze the features of image. And they include a variety of information about image's shape and location etc. So a lot of researches for detecting edges have been continued. Edge detection operators which were used at the early stage of the research were to utilize relations among neighboring pixels. These methods detect edge at all boundaries, therefore they perform edge detection twice about curves below some width such as line-edge. In the meantime, wavelet transform which is presented as a new technique of signal processing field provides multiscale edge detection and is being applied widely in many fields that analyze edge-like characteristic. Therefore, in this paper we detected line-edge with new 2-D wavelet function which is independent of line's width.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.2
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• pp.53-58
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• 2005

Points of sharp variations in images are the most important components when we analyze singularities of images. And they include a variety of information about the image's location and shape etc. So a lot of researches for detecting those edges have been continuing even now and at the early stage of the research, edge detection operators used relation among neighborhood pixels. However, such methods do not have excellent performance in the image which exists noise and can not detect edge selectively. In the meantime, the wavelet transform which is presented as a new technique of signal processing field is able to detect multiscale edge and is being applied widely in many fields that analyze singularities such as edge. For this reason, in this paper we detected image's line-edge elements with 2-D wavelet function, which is independent of line's width, in mixed noise environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.64-67
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• 2004

Points of sharp variations in images are the most important components when we analyze singularities of images. Therefore a lot of researches for detecting those edges have been continuing even now. However, existing methods do not have excellent performance in the image which exists noise and can not detect edge selectively. In the meantime, the wavelet transform which is presented as a new technique of signal processing field is able to detect multiscale edge and is being applied widely in many fields that analyze singularities such as edge. for this reason, this paper detected image's line-edge elements with 2-D wavelet function, which is independent of line's width, in noise environment.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.2
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• pp.91-97
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• 2007

Points of sharp variations in signals are the most important factors when analyzing the features of signals. And in the image, edges include diverse information such as the locations, shape and material. There have been a variety of researches on edge detections, among them, methods based on convolution in the spatial domain have been most popular. However at the early stage of the method, if the noise and many kinds of edges exist in the image, it is not easy to separate edges selectively from corrupted images by noise. In meantime, the wavelet transform for multiscale edge detection is being applied widely to analyze the properties of images in various fields. In this paper, we suggest a robust wavelet-based method, which selectively detects line-edge elements from images in the presence of noise.