A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction

By Mrinal Haloi, Samarendra Dandapat, Rohit Sinha. 2015

35 citations recorded by the source corpus.

In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.

Subfields: Health Information Management, Neurology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, methodology): “The methods [54, 28, 21, 29] use supervised approaches on image level without reporting a localization.”
Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification (2017, unknown): “[24] put forward mathematical morphology Gaussian scale space and anisotropic diffusion filter methods for image processing.”
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD (2018, methodology): “[2] introduced a method to detect cotton wool spots an exudates.”