A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms

Frame, A.J.; Undrill, P.E.; Cree, Michael J.; Olson, John A.; McHardy, K C; Sharp, P.; Forrester, John V.

doi:10.1016/s0010-4825(98)00011-0

Cited by 199 publications

(85 citation statements)

References 13 publications

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“…It has effects of leaving only features of a smaller scale than the filter, such as the vessels and the small hemorrhages in the image. 5,6 The images were then normalized between pixel values of 0 and 255. However, this method can destroy the larger features than the filter, so this program used HSV brightness correction instead of 3D Gaussian.…”

Section: Brightness Correction Using Gaussian Smoothing (3d Gaussian)mentioning

confidence: 99%

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A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

et al. 2010

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Section: Brightness Correction Using Gaussian Smoothing (3d Gaussian)mentioning

confidence: 99%

“…Spencer and Frame suggested a method using top-hat transformation and a matched filter. 4,5 Niemeijer et al 6 developed this research further and introduced the hybrid methods. Fleming et al 7 proposed a method for the detection of microaneurysms by using a watershed transform in fluorescein angiograms.…”

Section: Introductionmentioning

confidence: 99%

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

et al. 2010

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“…A mathematical morphology based approach using top-hat transform with a linear structural element is used to remove connected vasculatures and other retinal structures. Spencer et al [3] and Frame1998 et al [4] have extended this approach by adding shade correction as a pre-processing and match-filters of different sizes to detect MAs. However, the risk associated with intravenous contrast agents limit the usage of fluorescein angiograms for public DR screening programs [5].…”

Section: Introductionmentioning

confidence: 99%

“…A mathematical morphology based candidate microaneurysm extraction method is proposed by Spencer et al [7] and Frame et al [4]. The proposed approach removes bright-lesions by setting all positive pixel values of shade corrected image to zero.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

Adal¹,

Sidibé²,

Ali³

et al. 2014

Computer Methods and Programs in Biomedicine

103

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Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

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“…The high false positive rates are the main issue with current computer vision based screening methods. The most common classifiers used with computer vision are neural networks or statistical classifiers (Frame et al, 1998;Gardner et al, 1996). This paper takes an alternative more analytical approach by focusing on direct identification using accurate geometric models at the preprocessing stage.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Screening Using Computer Vision

Hann

Chase

Revie

et al. 2009

IFAC Proceedings Volumes

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Diabetic Retinopathy (DR) is one of the main causes of blindness and visual impairment in developed countries, stemming solely from diabetes mellitus. Current screening methods using fundus images rely on the experience of the operator as they are manually examined. Automated methods based on neural networks and other approaches have not provided sensitivity or specificity above 85%. This work presents a computer vision based method that directly identifies hard exudates and dot haemorrhages (DH) from 100 digital fundus images from a graded database of images using standard computer vision techniques, and clinical observation and knowledge. Sensitivity and specificity in diagnosis are 95-100% in both cases. Positive and negative prediction values (PPV, NPV) were 95-100% for both cases. The overall method is general, computationally efficient and suitable for further clinical trials to test both accuracy and the ability to the track DR status over time.

show abstract

A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms

Cited by 199 publications

References 13 publications

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

A Study on Hemorrhage Detection Using Hybrid Method in Fundus Images

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

Diabetic Retinopathy Screening Using Computer Vision

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