Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

Niemeijer, Meindert; Ginneken, Bram van; Cree, Michael J.; Mizutani, Atsushi; Quellec, Gwénolé; Sánchez, Clara I.; Zhang, Bob; Hornero, Roberto; Lamard, Mathieu; Muramatsu, Chisako; Wu, Xiangqian; Cazuguel, Guy; You, Jane; Mayo, A.; Qin, Li; Hatanaka, Yuji; Cochener, Béatrice; Roux, Christian; Karray, Fakhri; García, María M.; Fujita, Hiroshi; Abràmoff, Michael D.

doi:10.1109/tmi.2009.2033909

Cited by 450 publications

(214 citation statements)

References 35 publications

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“…The publicly available Retinopathy Online Challenge dataset (ROC) [17] was used to analyse the performance of the proposed algorithm. The ROC dataset is composed of both a Training and a Test dataset, composed of 50 images each.…”

Section: Resultsmentioning

confidence: 99%

Microaneurysms detection using a novel neighborhood analysis

Soares

Castelo‐Branco

Pinheiro

2014

Proceedings of the Ophthalmic Medical Image Analysis First International Workshop

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Abstract. The earliest sign of the diabetic retinopathy is the appearance of small red dots in retinal fundus images, designated by microaneurysms. In this paper a scale-space based method is proposed for the microaneurysms detection. Initially, the method performs a segmentation of the retinal vasculature and defines a global set of microaneurysms candidates, using both coarser and finer scales. Using the finer scales, a set of microaneurysms candidates are analysed in terms of shape and size. Then, a set of gaussian-shaped matched filters are used to reduce the number of false microaneurysms candidates. Each candidate is labeled as a true microaneurysm using a new neighborhood analysis method. The proposed algorithm was tested with the training Retinopathy Online Challenge (ROC) dataset, revealing a 47% Sensitivity with an average number of 37.9 false positives per image.

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Section: Resultsmentioning

confidence: 99%

Microaneurysms detection using a novel neighborhood analysis

Soares

Castelo‐Branco

Pinheiro

2014

Proceedings of the Ophthalmic Medical Image Analysis First International Workshop

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“…The LMRs of the pre-processed image is considering as MS candidate regions. In this process, we applied similar simple breadth-first search algorithm and calculation of gray-scale morphological reconstruction [4]. Sequentially pixels of the image are processed, and other neighboring images are compared.…”

Section: Introductionmentioning

confidence: 99%

Detection of Diabetic Retinopathy Using Optic Disc

Udayakumar¹,

S²,

Yogeshwaran³

et al. 2017

Asian J Pharm Clin Res

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This paper proposes a method for the automatic detection of optic disc in retinal images. In the diagnosis and grading, the essential step is recognition of optic disk for diabetic retinopathy. The analysis of directional cross-section profile focused on the local maximum pixel of pre-processed image is realized by the proposed method using optic disc detection. Each profile is implemented by peak detection and property like shape, size, and height of the peak are estimated. The statistical measure of the estimated values for the attributes, where the orientation of the cross-section changes the constitute feature used in morphological classification to exclude encourages candidates. The result is to find the patient is affected by diabetics or not.

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“…The final microaneurysms are then found by fusing the candidates of the pairs which are considered as optimal ensemble. This system is so far among the top performing methods in the Retinopathy Online Challenge database of images [29].…”

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

104

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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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Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs

Cited by 450 publications

References 35 publications

Microaneurysms detection using a novel neighborhood analysis

Microaneurysms detection using a novel neighborhood analysis

Detection of Diabetic Retinopathy Using Optic Disc

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

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