Simulation of fuzzy random variables
Information Sciences: an International Journal
A Fast Logical-Morphological Method to Segment Scratch - Type Objects
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Fuzzy Sets and Systems
Possibilistic mean value and variance of fuzzy numbers: some examples of application
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
An Automatic Hybrid Method for Retinal Blood Vessel Extraction
International Journal of Applied Mathematics and Computer Science - Selected Problems of Computer Science and Control
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
An approach to localize the retinal blood vessels using bit planes and centerline detection
Computer Methods and Programs in Biomedicine
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Image segmentation is a major problem in image processing, particularly in medical image analysis. A great number of segmentation procedures produce intermediate gray-scale images that can be understood as fuzzy sets. Additionally, some segmentation procedures tend to leave free tuning parameters (very influential in the final binary image) for the user. These different binary images can be easily aggregated (into a fuzzy set) by making use of fuzzy set theory. In any case, a single binary image is required so our interest is to associate a crisp set to a given fuzzy set in an intelligent and unsupervised manner. The main idea of this paper is to define the averages of a given fuzzy set by using different definitions of the mean of a random compact set. In particular, the average distance of Baddeley-Molchanov and the mean of Vorob'ev have been used. A theoretical study of some new definitions of fuzzy set averages has been performed. In particular, these averages have been obtained for L-R fuzzy numbers. Finally, we present a medical image application, that of retinal vessel detection. Some recent segmentation procedures have been revisited and modified using these new averages. The experimental results are very promising.