# Difference between revisions of "Publications/xu.13.ismm"

### From LRDE

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| authors = Yongchao Xu, Thierry Géraud, Laurent Najman |
| authors = Yongchao Xu, Thierry Géraud, Laurent Najman |
||

| title = Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity |
| title = Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity |
||

− | | booktitle = Mathematical Morphology and Its Application to Signal and Image Processing |
+ | | booktitle = Mathematical Morphology and Its Application to Signal and Image Processing – Proceedings of the 11th International Symposium on Mathematical Morphology (ISMM) |

| editors = C L Luengo Hendriks, G Borgefors, R Strand |
| editors = C L Luengo Hendriks, G Borgefors, R Strand |
||

| volume = 7883 |
| volume = 7883 |

## Latest revision as of 16:21, 5 January 2018

- Authors
- Yongchao Xu, Thierry Géraud, Laurent Najman
- Where
- Mathematical Morphology and Its Application to Signal and Image Processing – Proceedings of the 11th International Symposium on Mathematical Morphology (ISMM)
- Place
- Uppsala, Sweden
- Type
- inproceedings
- Publisher
- Springer
- Projects
- Olena
- Keywords
- Image
- Date
- 2013-03-14

## Abstract

Connected filtering is a popular strategy that relies on tree- based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the treeseen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes built from the image. Such a processing, that we called shape-based morphology, is a generalization of the existing tree-based connected operators. In this paper, two different applications are studied: in the first one, we apply our framework to blood vessels segmentation in retinal images. In the second one, we propose an extension of constrained connectivity. In both cases, quantitative evaluations demonstrate that shape-based filtering, a mere filtering step that we compare to more evolved processingsachieves state-of-the-art results.

## Documents

## Bibtex (lrde.bib)

@InProceedings{ xu.13.ismm, author = {Yongchao Xu and Thierry G\'eraud and Laurent Najman}, title = {Two applications of shape-based morphology: blood vessels segmentation and a generalization of constrained connectivity}, booktitle = {Mathematical Morphology and Its Application to Signal and Image Processing -- Proceedings of the 11th International Symposium on Mathematical Morphology (ISMM)}, year = 2013, editor = {C.L. Luengo Hendriks and G. Borgefors and R. Strand}, volume = 7883, series = {Lecture Notes in Computer Science Series}, address = {Uppsala, Sweden}, publisher = {Springer}, pages = {390--401}, abstract = {Connected filtering is a popular strategy that relies on tree- based image representations: for example, one can compute an attribute on each node of the tree and keep only the nodes for which the attribute is sufficiently strong. This operation can be seen as a thresholding of the tree, seen as a graph whose nodes are weighted by the attribute. Rather than being satisfied with a mere thresholding, we propose to expand on this idea, and to apply connected filters on this latest graph. Consequently, the filtering is done not in the space of the image, but on the space of shapes built from the image. Such a processing, that we called shape-based morphology, is a generalization of the existing tree-based connected operators. In this paper, two different applications are studied: in the first one, we apply our framework to blood vessels segmentation in retinal images. In the second one, we propose an extension of constrained connectivity. In both cases, quantitative evaluations demonstrate that shape-based filtering, a mere filtering step that we compare to more evolved processings, achieves state-of-the-art results.} }