Difference between revisions of "Publications/boutry.21.dgmm.2"
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 title = A New Matching Algorithm between Trees of Shapes and its Application to Brain Tumor Segmentation 
 title = A New Matching Algorithm between Trees of Shapes and its Application to Brain Tumor Segmentation 

 booktitle = Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) 
 booktitle = Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) 

+   pages = 67 to 78 

 address = Uppsala, Sweden 
 address = Uppsala, Sweden 

+   series = Lecture Notes in Computer Science 

⚫   abstract = Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. 

+   volume = 12708 

+   publisher = Springer 

⚫   abstract = Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. Howevernone of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map. 

+   lrdepaper = http://www.lrde.epita.fr/dload/papers/boutry.21.dgmm.2.pdf 

 lrdeprojects = Olena 
 lrdeprojects = Olena 

 lrdenewsdate = 20210302 
 lrdenewsdate = 20210302 

−   note = To appear 

 type = inproceedings 
 type = inproceedings 

 id = boutry.21.dgmm.2 
 id = boutry.21.dgmm.2 

+   identifier = doi:10.1007/9783030766573_4 

 bibtex = 
 bibtex = 

@InProceedings<nowiki>{</nowiki> boutry.21.dgmm.2, 
@InProceedings<nowiki>{</nowiki> boutry.21.dgmm.2, 

Line 20:  Line 25:  
Discrete Geometry and Mathematical Morphology (DGMM)<nowiki>}</nowiki>, 
Discrete Geometry and Mathematical Morphology (DGMM)<nowiki>}</nowiki>, 

year = 2021, 
year = 2021, 

−  month = 
+  month = may, 
+  pages = <nowiki>{</nowiki>6778<nowiki>}</nowiki>, 

address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, 
address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, 

+  series = <nowiki>{</nowiki>Lecture Notes in Computer Science<nowiki>}</nowiki>, 

+  volume = <nowiki>{</nowiki>12708<nowiki>}</nowiki>, 

+  publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, 

abstract = <nowiki>{</nowiki>Many approaches exist to compute the distance between two 
abstract = <nowiki>{</nowiki>Many approaches exist to compute the distance between two 

trees in pattern recognition. These trees can be structures 
trees in pattern recognition. These trees can be structures 

Line 36:  Line 45:  
provide only a segmentation but also the tree of shapes 
provide only a segmentation but also the tree of shapes 

corresponding to the segmentation and its depth map.<nowiki>}</nowiki>, 
corresponding to the segmentation and its depth map.<nowiki>}</nowiki>, 

−  +  doi = <nowiki>{</nowiki>10.1007/9783030766573_4<nowiki>}</nowiki> 

<nowiki>}</nowiki> 
<nowiki>}</nowiki> 

Latest revision as of 10:54, 8 September 2021
 Authors
 Nicolas Boutry, Thierry Géraud
 Where
 Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM)
 Place
 Uppsala, Sweden
 Type
 inproceedings
 Publisher
 Springer
 Projects
 Olena
 Date
 20210302
Abstract
Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. Howevernone of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map.
Documents
Bibtex (lrde.bib)
@InProceedings{ boutry.21.dgmm.2, author = {Nicolas Boutry and Thierry G\'eraud}, title = {A New Matching Algorithm between Trees of Shapes and its Application to Brain Tumor Segmentation}, booktitle = {Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM)}, year = 2021, month = may, pages = {6778}, address = {Uppsala, Sweden}, series = {Lecture Notes in Computer Science}, volume = {12708}, publisher = {Springer}, abstract = {Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. However, none of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map.}, doi = {10.1007/9783030766573_4} }