Computers and Biomedical Research
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
ISMDA '02 Proceedings of the Third International Symposium on Medical Data Analysis
Automatic Segmentation of Microaneurysms in Retinal Angiograms of Diabetic Patients
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
A Model-Based Approach for Automated Feature Extraction in Fundus Images
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
Automated Microaneurysm Segmentation and Detection using Generalized Eigenvectors
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Separation of the retinal vascular graph in arteries and veins based upon structural knowledge
Image and Vision Computing
Detection of microaneurysms using multi-scale correlation coefficients
Pattern Recognition
A region based algorithm for vessel detection in retinal images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Microaneurysms (MAs) detection is a critical step in diabetic retinopathy screening, since MAs are the earliest visible warning of potential future problems. A variety of thresholding based algorithms have been proposed for MAs detection in mass screening. Most of them process fundus images globally without a mechanism to take into account the local properties and changes. Their performance is often susceptible to nonuniform illumination and locations of MAs in different retinal regions. To keep sensitivity at a relatively high level, a low grey value threshold must be applied to the entire image globally, resulting in a much lower specificity in MAs detection. Therefore, post-processing steps, such as, feature extraction and classification, must be followed to improve the specificity at the cost of sensitivity. In order to address this problem, a local adaptive algorithm is proposed for automatic detection of MAs, where multiple subregions of each image are automatically analyzed to adapt to local intensity variation and properties, and furthermore prior structural features and pathology, such as, region and location information of vessel, optic disk and hard exudate are incorporated to further improve the detection accuracy. This algorithm effectively improves the specificity of MAs detection, without sacrificing the achieved sensitivity. It has potential to be used for automatic level-one grading of diabetic retinopathy screening.