Fuzzy-ASM Based Automated Skull Stripping Method from Infantile Brain MR Images

Author

Kobashi, Syoji ; Fujimoto, Yuko ; Ogawa, Masayo ; Ando, Kumiko ; Ishikura, Reiichi ; Kondo, Katsuya ; Hirota, Shozo ; Hata, Yutaka

Author_Institution

Univ. of Hyogo, Kobe

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

632

Lastpage

632

Abstract

Automated stripping of skulls from infantile brain MR images is the fundamental work to visualize cerebral surface and to measure cerebral volumes. They are important to evaluate cerebral diseases because most cerebral diseases cause morphometric changes in cerebrum. This study proposes a novel image segmentation method based on fuzzy rule-based active surface model. The proposed method was validated by applying it to two neonatal (3W and 4W) and six infantile (5W to 4Y2M) subjects. The mean sensitivity was 98.84 %, and false-positive rate was 1.21 %, and the cerebral surface was visualized well.

Keywords

biomedical MRI; brain; image segmentation; medical image processing; active surface model; cerebral diseases evaluation; cerebral surface visualization; cerebral volume measurement; fuzzy-ASM based automated skull stripping method; image segmentation method; infantile brain MR images; Biomedical imaging; Deformable models; Diseases; Image analysis; Pediatrics; Rough surfaces; Skull; Surface morphology; Surface roughness; Visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing, 2007. GRC 2007. IEEE International Conference on

Conference_Location

Fremont, CA

Print_ISBN

978-0-7695-3032-1

Type

conf

DOI

10.1109/GrC.2007.63

Filename

4403176