High speed detection of retinal blood vessels in fundus image using phase congruency

Amin, M. Ashraful; Yan, Hong

doi:10.1007/s00500-010-0574-2

Cited by 51 publications

(24 citation statements)

References 22 publications

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“…The proposed technique presents a higher mean accuracies and mean sensitivity while compared to the previously proposed phase congruence based technique by Dai et al[3]. Amin and Yan[1] present a lower mean accuracy of 0.92 while compared to mean accuracy value 0.94161 of the proposed technique. However, the phase congruence technique proposed by Zhu[25] presented only a…”

contrasting

confidence: 50%

“…where the amplitude A k is the k th Fourier component, and φ k (x) is the local phase at location k. The amplitude weighted mean local phase angle of all the Fourier terms at the position under consideration yields the value φ(x) that maximizes equation (1). Computing the cosine of the difference between the actual phase angle of a frequency component and the weighted mean, φ(x), gives the Taylor expansion cos(x) ≈ 1 − x 2 /2 for lower values of x.…”

Section: A Preprocessing Using Phase Congruencementioning

confidence: 99%

“…Amin and Yan [1] implemented the detection of retinal blood vessels using phase congruency at an high speed with a mean accuracy of 0.92 for DRIVE database. The threshold values used were however not dynamically computed but ranges from 0.32 to 0.37.…”

Section: Related Workmentioning

confidence: 99%

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Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Mapayi¹,

Viriri²,

Tapamo

2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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The detection and analysis of retinal vessels in ophthalmology is of great use in the diagnosis and progression monitoring of diabetic retinopathy. Automatic Detection of the vessel network has however been challenging due to noise from uneven contrast and illumination during the retinal image acquisition process. This paper presents a robust segmentation technique that combines phase congruence and Gray level co-occurrence matrix (GLCM) sum entropy information for the detection of vessel network. While compared with the previously used techniques on DRIVE database, the proposed technique yields high mean sensitivity and mean accuracy rates in the same range of very good specificity.

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confidence: 50%

Section: A Preprocessing Using Phase Congruencementioning

confidence: 99%

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Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Mapayi¹,

Viriri²,

Tapamo

2015

2015 IEEE International Conference on Industrial Technology (ICIT)

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“…A hybrid model of the MF and ant colony algorithm for retinal vessel segmentation is proposed by Cinsdikici and Aydin [30]. A highspeed detection of retinal blood vessels using phase congruency and a bank of log-Gabor filters has been proposed by Amin and Yan [31]. Zana and Klein [32] combine morphological filters and cross-curvature evaluation to segment vessel-like patterns.…”

Section: Related Workmentioning

confidence: 99%

Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

2012

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The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.

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“…The work of Amin et al [32] is a method based on phase congruency and thresholding. Phase congruency is invariant to image brightness or contrast and is obtained using Log-Gabor wavelets.…”

Section: A Vessel Segmentation and Vessel Centerline Extractionmentioning

confidence: 99%

A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images

Sreejini¹,

Govindan²

2015

IJIGSP

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Abstract-Ophthalmology is the study of structures, functions, treatment and disorders of eye. Computer aided analysis of retina images is still an open research area. Numerous efforts have been made to automate the analysis of retina images. This paper presents a review of various existing research in detection of anatomical structures in retina and lesions for the diagnosis of diabetic retinopathy (DR). The research in detection of anatomical structures is further divided into subcategories, namely, vessel segmentation and vessel centerline extraction, optic disc segmentation and localization, and fovea/ macula detection and extraction. Various research works in each of the categories are reviewed highlighting the techniques employed and comparing the performance figures obtained. The issues/ lacuna of various approaches are brought out. The following major observations are made: Most of the vessel detection algorithms fail to extract small thin vessels having low contrast. It is difficult to detect vessels at regions where close vessels are merged, at regions of missing of small vessels, at optic disc regions, and at regions of pathology. Machine learning based approaches for blood vessel tracing requires long processing time. It is difficult to detect optic disc radius or boundary with simple blood vessel tracing. Automatic detection of fovea and macular region extraction becomes complicated due to nonuniform illuminations while imaging and diseases of the eyes. Techniques requiring prior knowledge leads to complexity.Most lesion detection algorithms underperform due to wide variations in the color of fundus images arising out of variations in the degree of pigmentation and presence of choroid.

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High speed detection of retinal blood vessels in fundus image using phase congruency

Cited by 51 publications

References 22 publications

Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Retinal vessel segmentation based on phase congruence and GLCM sum-entropy

Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

A Review of Computer Aided Detection of Anatomical Structures and Lesions of DR from Color Retina Images

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