Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs

By Gwenolé Quellec, Mathieu Lamard, P. Josselin, G. Cazuguel, Béatrice Cochener, Christian Roux. IEEE Transactions on Medical Imaging, 2008

383 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f0cb5bfbe32dd5f3a0bed2e4e39e656daaee5a8a

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In Biomedical Engineering

In Ophthalmology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge. (2020, positive): “Apart from these approaches, in case of MAs, most initial studies have shown effectiveness of template matching ( Quellec et al., 2008 ), entropy thresholding ( Das et al., 2015 ), radon space ( Giancardo et al., 2011 ), sparse representation ( Zhang et al., 2012; Javidi et al., 2017 ), Hessian…”
Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs (2010, methodology): “[23] described a supervised microaneurysm detection method based on template matching in wavelet-subbands.”
Retinal Imaging and Image Analysis (2010, neutral): “One group decided to drop out of the competition after the results were announced, and the remainder allowed their performance to be discussed publicly [89], [106]–[108].”
Identification and classification of microaneurysms for early detection of diabetic retinopathy (2013, methodology): “Quellec et al. [12] presented a template matching based method using an optimal wavelet transform.”
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images (2016, neutral): “based onmorphological operations [18],wavelet operations [19]”
TeleOphta: Machine learning and image processing methods for teleophthalmology (2013, methodology): “…the signs of DR, and of other retinal pathologies The proposed solution relies on wavelet-based image characterizations developed in previous works [4 Each image in a patient record is divided into patches and a vector of image characterizations, called signature, is extracted from each patch A…”
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection (2010, methodology): “most popular methods for feature extraction are morphology in the detection of exudates [18], [19], Gabor filters in the detection and differentiation of bright lesions [15], and the classification of retinal images [20], wavelet transforms in the detection of MAs by Quellec et al. [ 21 ], [22], and”
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy (2017, methodology): “The development dataset used consists of images obtained from patients who presented for DR screening at EyePACSaffiliated clinics, 3 eye hospitals in India (Aravind Eye Hospital, Sankara Nethralaya, and Narayana Nethralaya), and the publicly available Messidor-2 dataset [17, 18].”
DREAM: Diabetic Retinopathy Analysis Using Machine Learning (2014, methodology): “In [6], MA were detected using template matching in wavelet subbands followed by thresholding.”