Title :
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
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;
Conference_Titel :
Biomedical Engineering and Informatics (BMEI), 2011 4th International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9351-7
DOI :
10.1109/BMEI.2011.6098610
