Bayesian Object Localisation in Images
International Journal of Computer Vision
Classification and Localisation of Diabetic-Related Eye Disease
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Computer Methods and Programs in Biomedicine
Application of transferable belief model to navigation system
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Morphological neural networks and vision based simultaneous localization and mapping
Integrated Computer-Aided Engineering - Artificial Neural Networks
Integrated Computer-Aided Engineering
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Automatic image search based on improved feature descriptors and decision tree
Integrated Computer-Aided Engineering
A scatter method for data and variable importance evaluation
Integrated Computer-Aided Engineering
Comparison of entity with fuzzy data types in fuzzy object-oriented databases
Integrated Computer-Aided Engineering
Improving fusion with optimal weight selection in Face Recognition
Integrated Computer-Aided Engineering
A supervised method for microcalcification cluster diagnosis
Integrated Computer-Aided Engineering
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Often, in the clinical setting, the severity of eye diseases is graded according to the number of lesions and their spatial relation within the retina. Detection of the optic disc OD and fovea is an important task in retinal imaging due to its significance in both clinical and image understanding tasks. Moreover, spatial relations between these structures are used to define which of the two eyes is under examination and to delineate the region of the macula. To detect these structures, several approaches have been proposed, primarily based on brightness and shape information. Recently, approaches that combine brightness, shape and information regarding the vascular structures have been used with good results. Nevertheless, they have been used in the detection of the OD/fovea on normal fundus images and do not account for properties specific to fluorescein angiography FA, such as intensity variations and the presence of lesions, which may complicate the application of the FA procedure. In this paper, a method which combines brightness information, namely, local mean intensity and local mean intensity variation, and geometric information from the major blood vessels is used in the simultaneous detection of the OD and fovea, and in delineating the macular region in FA. The benefits of the proposed combination-based approach, in terms of accuracy, are demonstrated by comparing the results with those of intensity-and shape-based techniques, when used on their own. The image set employed to evaluate the proposed technique consists of both healthy and unhealthy retinas. The techniques discussed in this contribution result to a robust, fast and accurate system for macular delineation, which is able to simultaneously detect the OD/fovea at different stages of the FA and also in the presence of lesions such as drusens and micro aneurysms.