Automatic detection and segmentation of brain tumor using fuzzy classification and deformable models

Author

Yang, Wang ; Siliang, Ma

Author_Institution

Dept. of Comput. Math., Jilin Univ., Changchun, China

Volume

3

fYear

2011

fDate

15-17 Oct. 2011

Firstpage

1680

Lastpage

1683

Abstract

We propose a new general method for segmenting brain tumors in 3D magnetic resonance images. Our method is applicable to different types of tumors. First, the brain is segmented using a new approach, robust to the presence of tumors. Then a tumor detection is performed, based on improved fuzzy classification. Its result constitutes the initialization of a segmentation method based on a deformable model, leading to a precise segmentation of the tumors. Imprecision and variability are taken into account at all levels, using appropriate fuzzy models. The result obtained on different types of tumors have been evaluated by comparison with manual segmentations.

Keywords

biomedical MRI; brain; fuzzy reasoning; image classification; image segmentation; medical image processing; tumours; 3D magnetic resonance images; automatic brain tumor detection; brain tumor segmentation; deformable models; fuzzy classification; imprecision; variability; Biomedical engineering; Conferences; Informatics; deformable model; hram tumor; improved kernel fuzzy c-mean; segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-9351-7

Type

conf

DOI

10.1109/BMEI.2011.6098610

Filename

6098610