On the Adaptive Detection of Blood Vessels in Retinal Images

Wu, Di; Zhang, Ming; Liu, Jyh-Charn; Bauman, Wendall

doi:10.1109/tbme.2005.862571

Cited by 90 publications

(40 citation statements)

References 11 publications

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“…Texture generally describes second order property of surfaces and scenes, measured over image intensities [42]. Considering the difference of texture between the drusen and background researchers have applied a bank of Gabor filters, wavelet feature extraction, amplitude modulation -frequency modulation to extract texture features and applied suitable classification of these features.…”

Section: Texture Based Segmentationmentioning

confidence: 99%

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

Bhuiyan

Xiao²,

Kanagasingam³

2014

J Comput Sci Syst Biol

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Age-related macular degeneration (AMD), a retinal disorder is the leading cause of vision loss in elderly people in developed countries. Currently, colour fundus imaging is used as a gold standard to assess individual for initial screening of AMD. In this paper, we examine the existing techniques for AMD screening using colour fundus imaging. The aim of the paper is to analyse and review the automated and semi-automated methods for detecting AMD pathologies such as drusen and geographic atrophy (GA) using colour fundus imaging. We intend to provide a brief description, highlighting the key points of the performance measurement and a framework for the existing research. We emphasize that to date; existing drug treatment can stop the progression of AMD and there is no drug or treatment to cure AMD. Therefore, the only feasible option is to prevent the incidence of AMD and avoid this fatal eye disease as well as unnecessary vision loss. For this, a mass screening system that enables remote diagnosis of AMD should be implemented for taking preventative measures in the early stage of AMD. Considering this, we discuss the open problems for automated AMD identification systems using colour fundus imaging and provided future directions for remote diagnosis and screening systems using colour fundus imaging and telemedicine platform.

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Section: Texture Based Segmentationmentioning

confidence: 99%

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

Bhuiyan

Xiao²,

Kanagasingam³

2014

J Comput Sci Syst Biol

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“…Poor parameter selection can lead to the suppression of lines that should otherwise be enhanced. Previous studies have not focused on the effect of normalization on Gabor filter effectiveness and only a few normalization techniques have been proposed [19], [20], [21].…”

Section: Emailmentioning

confidence: 99%

“…The effectiveness of the filters is however not tested on normal unsegmented retinal images which are more complex. Wu et al [21] use adaptive contrast enhancement that is based on the standard deviation of a Gabor Filter Response (GFR) image window to highlight vessels. Two randomly selected images from the STARE data set are used for parameter training.…”

Section: Literature Surveymentioning

confidence: 99%

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Gwetu¹,

Tapamo²,

Viriri³

2014

SACJ

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Although computerized retinal image blood vessel segmentation has been extensively researched, there is still room for improvement in the quality of the segmented images. Since retinal image analysis is still widely used in the diagnosis of diabetic retinopathy, efficient and accurate image characterization techniques are required. Previous work has mainly focused on improving segmentation accuracy rates with little regard to the false positives that are produced by illumination variation. This research work presents a hybrid approach towards the segmentation of retinal blood vessels. New approaches towards the reduction of background illumination variation are proposed using normalized Gabor filtering. These are the base-offset encoding and a modified version of an existing zero-integral kernel technique. The valley emphasis automatic thresholding scheme is used to segment the Gabor response images. Experiments are conducted on the DRIVE and STARE retinal image data sets. Accuracy rates of up to 94% are achieved through the zero-integral and base offset methods. This is comparable with results from literature, where the same data sets are segmented using other classification techniques. The median-offset method is found to most effectively reduce background illumination variation.

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“…Considering the conducted research, literature is full of examples [3][4][5][6][7][8][9][10] on vasculature segmentation, detection, and other kinds of analysis employing especially supervised/unsupervised classification of pixels in retinal fundus images [11][12][13][14][15][16][17][18][19]. Marin et al [14] and Soares et al [15] presented two different supervised methods for segmentation of retinal vasculature by using moment invariant-based features and 2-D Gabor filters, respectively.…”

Section: Introductionmentioning

confidence: 99%

Structure-based level set method for automatic retinal vasculature segmentation

et al. 2014

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Segmentation of vasculature in retinal fundus image by level set methods employing classical edge detection methodologies is a tedious task. In this study, a revised level set-based retinal vasculature segmentation approach is proposed. During preprocessing, intensity inhomogeneity on the green channel of input image is corrected by utilizing all image channels, generating more efficient results compared to methods utilizing only one (green) channel. A structure-based level set method employing a modified phase map is introduced to obtain accurate skeletonization and segmentation of the retinal vasculature. The seed points around vessels are selected and the level sets are initialized automatically. Furthermore, the proposed method introduces an improved zero-level contour regularization term which is more appropriate than the ones introduced by other methods for vasculature structures. We conducted the experiments on our own dataset, as well as two publicly available datasets. The results show that the proposed method segments retinal vessels accurately and its performance is comparable to state-of-the-art supervised/unsupervised segmentation techniques.

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On the Adaptive Detection of Blood Vessels in Retinal Images

Cited by 90 publications

References 11 publications

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

A Review of Disease Grading and Remote Diagnosis for Sight Threatening Eye Condition: Age Related Macular Degeneration

Segmentation of retinal blood vessels using normalized Gabor filters and automatic thresholding

Structure-based level set method for automatic retinal vasculature segmentation

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