Automatic Optic Disc and Fovea Detection in Retinal Images Using Super-Elliptical Convergence Index Filters

Dashtbozorg, Behdad; Zhang, Jiong; Huang, Fan; Romeny, Bart M. ter Haar

doi:10.1007/978-3-319-41501-7_78

Cited by 23 publications

(19 citation statements)

References 18 publications

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“…The experimental results of proposed approach are compared with nine existing approaches such as Shabbir et al (), Akram et al (), Kusakunniran et al (), Colomer, Naranjo, Janvier, and Mossi (), Issac et al (), Dashtbozorg, Zhang, Huang, and ter Haar Romeny (), Acharya et al (), and Kaur and Mittal (). Gabor wavelets and Otsu method are applied only for detection of soft‐exudates.…”

Section: Resultsmentioning

confidence: 99%

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Diabetic retinopathy detection and classification using hybrid feature set

Amin

Sharif

Rehman

et al. 2018

Microscopy Res & Technique

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Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).

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Section: Resultsmentioning

confidence: 99%

“…It obtains 97.3 and 96.74% ACC (Marin et al, ). Super‐elliptical convergence index filters are used for exudates detection (Dashtbozorg et al, ). The discrete wavelet transform is used for exudates segmentation (Acharya et al, ).…”

Section: Resultsmentioning

confidence: 99%

Diabetic retinopathy detection and classification using hybrid feature set

Amin

Sharif

Rehman

et al. 2018

Microscopy Res & Technique

View full text Add to dashboard Cite

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“…In the framework of the RetintaCheck project 1 (Dashtbozorg et al 2016a), we have developed several retinal image analysis tools including automatic retinal vessel enhancement (Zhang et al 2016b;AbbasiSureshjani et al 2015), segmentation (Zhang et al 2015(Zhang et al , 2016a, OD/fovea detection (Dashtbozorg et al 2016b), artery/vein classification (Huang et al 2017a(Huang et al , 2018, calibre calculation (Huang et al 2017b), vessel curvature measurement (Bekkers et al 2015) and fractal analysis (Huang et al 2015(Huang et al , 2016 to obtain important vessel biomarkers like central retinal arterial equivalent (CRAE), central retinal venous equivalent (CRVE), the arterial-venous diameter ratio (AVR), vessel tortuosity and FD.…”

Section: Methodsmentioning

confidence: 99%

A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases

Zhang

Dashtbozorg

Huang

et al. 2018

Computer Methods in Biomechanics and Biomedical Engineering: Im

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This paper presents an automated system for extracting retinal vascular biomarkers for early detection of diabetes. The proposed retinal vessel enhancement, segmentation, optic disc (OD) and fovea detection algorithms provide fundamental tools for extracting the vascular network within the predefined region of interest. Based on that, the artery/vein classification, vessel width, tortuosity and fractal dimension measurement tools are used to assess a large number of quantitative vascular biomarkers. We evaluate our pipeline module by module against human annotations. The results indicate that our automated system is robust to the localisation of OD and fovea, segmentation of vessels and classification of arteries/veins. The proposed pipeline helps to increase the effectiveness of the biomarkers extraction and analysis for the early diabetes, and therefore, has the large potential of being further incorporated into a computer-aided diagnosis system.

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“…Several CI filters have been proposed according to the way how the support region is defined [31]. Among different CI filters, the adaptive ring filter (ARF) [32], the sliding band filter (SBF) [33] and super-elliptical filter (SEF) [34] are more suitable for the MA detection, since they can be parameterized to use a narrow band for spotting the donut-shaped objects in the gradient-weighted images.…”

Section: Feature Extractionmentioning

confidence: 99%

Retinal Microaneurysms Detection Using Local Convergence Index Features

Dashtbozorg

Zhang

Huang

et al. 2018

IEEE Trans. on Image Process.

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Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.

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Automatic Optic Disc and Fovea Detection in Retinal Images Using Super-Elliptical Convergence Index Filters

Cited by 23 publications

References 18 publications

Diabetic retinopathy detection and classification using hybrid feature set

Diabetic retinopathy detection and classification using hybrid feature set

A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases

Retinal Microaneurysms Detection Using Local Convergence Index Features

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