3D brain tumor segmentation using fuzzy classification and deformable models

Hassan Khotanlou; Jamal Atif; Olivier Colliot; Isabelle Bloch

doi:10.1007/11676935_39

3D brain tumor segmentation using fuzzy classification and deformable models

Hassan Khotanlou
, Jamal Atif
, Olivier Colliot
, Isabelle Bloch

CNRS LTCI
McConnell Brain Imaging Centre

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.

Original language	English
Title of host publication	Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers
Pages	312-318
Number of pages	7
DOIs	https://doi.org/10.1007/11676935_39
Publication status	Published - 23 Jun 2006
Externally published	Yes
Event	6th International Workshop - Fuzzy Logic and Applications - Crema, Italy Duration: 15 Sept 2005 → 17 Sept 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3849 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	6th International Workshop - Fuzzy Logic and Applications
Country/Territory	Italy
City	Crema
Period	15/09/05 → 17/09/05

Access to Document

10.1007/11676935_39

Cite this

Khotanlou, H., Atif, J., Colliot, O., & Bloch, I. (2006). 3D brain tumor segmentation using fuzzy classification and deformable models. In Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers (pp. 312-318). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3849 LNAI). https://doi.org/10.1007/11676935_39

Khotanlou, Hassan ; Atif, Jamal ; Colliot, Olivier et al. / 3D brain tumor segmentation using fuzzy classification and deformable models. Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers. 2006. pp. 312-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "3D brain tumor segmentation using fuzzy classification and deformable models",

abstract = "A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.",

author = "Hassan Khotanlou and Jamal Atif and Olivier Colliot and Isabelle Bloch",

year = "2006",

month = jun,

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doi = "10.1007/11676935\_39",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "312--318",

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Khotanlou, H, Atif, J, Colliot, O & Bloch, I 2006, 3D brain tumor segmentation using fuzzy classification and deformable models. in Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3849 LNAI, pp. 312-318, 6th International Workshop - Fuzzy Logic and Applications, Crema, Italy, 15/09/05. https://doi.org/10.1007/11676935_39

3D brain tumor segmentation using fuzzy classification and deformable models. / Khotanlou, Hassan; Atif, Jamal; Colliot, Olivier et al.
Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers. 2006. p. 312-318 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3849 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Atif, Jamal

AU - Colliot, Olivier

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N2 - A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.

AB - A new method that automatically detects and segments brain tumors in 3D MR images is presented. An initial detection is performed by a fuzzy possibilistic clustering technique and morphological operations, while a deformable model is used to achieve a precise segmentation. This method has been successfully applied on five 3D images with tumors of different sizes and different locations, showing that the combination of region-based and contour-based methods improves the segmentation of brain tumors.

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M3 - Conference contribution

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers

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Khotanlou H, Atif J, Colliot O, Bloch I. 3D brain tumor segmentation using fuzzy classification and deformable models. In Fuzzy Logic and Applications - 6th International Workshop, WILF 2005, Revised Selected Papers. 2006. p. 312-318. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11676935_39

3D brain tumor segmentation using fuzzy classification and deformable models

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