Name
Abstract | ||
|---|---|---|
Medical images edge detection is one of the most important pre-processing steps in medical image segmentation and 3D reconstruction. In this paper, an edge detection algorithm using an uninorm-based fuzzy morphology is proposed. It is shown that this algorithm is robust when it is applied to different types of noisy images. It improves the results of other well-known algorithms including classical algorithms of edge detection, as well as fuzzy-morphology based ones using the {\L}ukasiewicz t-norm and umbra approach. It detects detailed edge features and thin edges of medical images corrupted by impulse or gaussian noise. Moreover, some different objective measures have been used to evaluate the filtered results obtaining for our approach better values than for other approaches. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.118 | ISDA |
Keywords | Field | DocType |
different objective measure,approach better value,edge detection,thin edge,classical algorithm,medical image segmentation,edge detection algorithm,uninorm fuzzy morphological gradient,medical image,medical images edge detection,noisy image,detailed edge feature,noise reduction,gaussian noise,image segmentation,image reconstruction,biomedical imaging,fuzzy set theory,3d reconstruction,signal to noise ratio,mathematical morphology,detectors,morphology,feature extraction | Iterative reconstruction,Canny edge detector,Deriche edge detector,Pattern recognition,Computer science,Edge detection,Mathematical morphology,Image segmentation,Artificial intelligence,Morphological gradient,Gaussian noise,Machine learning | Conference |
ISSN | ISBN | Citations |
2164-7143 | 978-0-7695-3872-3 | 5 |
PageRank | References | Authors |
0.75 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manuel González Hidalgo | 1 | 99 | 18.29 |
| Arnau Mir-Torres | 2 | 20 | 2.25 |
| Joan Torrens Sastre | 3 | 17 | 1.51 |