0000000000379140

AUTHOR

Shrinivasan Sankar

showing 2 related works from this author

Classification of SD-OCT Volumes for DME Detection: An Anomaly Detection Approach

2016

International audience; Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binar…

SD-OCTgenetic structuresComputer scienceLocal binary patternsDiabetic macular edema[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]01 natural sciences010309 optics03 medical and health sciencesGaussian Mixture Model0302 clinical medicine[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Optical coherence tomography0103 physical sciencesmedicineComputer visionSensitivity (control systems)Local Binary PatternBlindnessmedicine.diagnostic_testbusiness.industryAnomaly (natural sciences)[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]medicine.diseaseMixture modeleye diseasesDiabetic Macular EdemaOutlierAnomaly detectionArtificial intelligencebusiness030217 neurology & neurosurgery
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Classification of SD-OCT Volumes with LBP: Application to DME Detection

2015

International audience; This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with Diabetic Macular Edema (DME) versus normal subjects. Our method is based on Local Binary Patterns (LBP) features to describe the texture of Optical Coherence Tomography (OCT) images and we compare different LBP features extraction approaches to compute a single signature for the whole OCT volume. Experimental results with two datasets of respectively 32 and 30 OCT volumes show that regardless of using low or high level representations, features derived from LBP texture have highly discriminative power. Moreover, the experimen…

genetic structuresLocal binary patternsComputer scienceDiabetic macular edemaSpectral domain02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineOptical coherence tomographyDiscriminative modelLBP0202 electrical engineering electronic engineering information engineeringmedicineDMEComputer vision[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingmedicine.diagnostic_testbusiness.industryeye diseasesDiabetic Macular EdemaOCT020201 artificial intelligence & image processingArtificial intelligencesense organsOptical Coherence Tomographybusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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