DocumentCode :
2188583
Title :
A New Fast Chinese Visible Human Brain Skull Stripping Method
Author :
Chen Yunjie ; Zhang Jianwei ; Wang Shunfeng
Author_Institution :
Dept. of math, Nanjing Univ. of Inf. Sci. & Technol., Nanjing, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Image data of the entire cadaver from the chinese visible human is being used to produce three-dimensional images and software for human anatomy research. The anatomy of human brain is more complicated. With the effect of noise, bias field, and fake grey matters, it is a challenging task to build a digital three dimensional representation of a human brain. In order to obtain more accurate representation, the brain regions must be separated from highly variable background regions to obtain a suitable stack of segmentation images. We use adapted Gauss mixture model as a promising starting point for a sophisticated segmentation framework of color images within 3-dimensions. The model can classify images meanwhile estimate the bias field. For the effect of the fake grey matters, a proper image preprocessing strategy turned out to be necessary for accurate and robust segmentation results. We present a complete high resolution and accurate segmentation of the CVH brain. Based on these images, 3D representation is presented.
Keywords :
Gaussian processes; brain; image denoising; image representation; image segmentation; medical image processing; adapted Gauss mixture model; bias field effect; chinese visible human; digital three dimensional human brain representation; fake grey matter effect; human brain anatomy; image denoising; image segmentation; noise effect; skull stripping method; Anatomy; Brain; Cadaver; Color; Colored noise; Gaussian processes; Humans; Image reconstruction; Image segmentation; Skull;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image and Signal Processing, 2009. CISP '09. 2nd International Congress on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4129-7
Electronic_ISBN :
978-1-4244-4131-0
Type :
conf
DOI :
10.1109/CISP.2009.5305306
Filename :
5305306
Link To Document :
بازگشت