DocumentCode :
1822472
Title :
Medical Image Segmentation Using Feature-based GVF Snake
Author :
Ng, H.P. ; Foong, K.W.C. ; Ong, S.H. ; Goh, P.S. ; Nowinski, W.L.
Author_Institution :
NUS, Singapore
fYear :
2007
fDate :
22-26 Aug. 2007
Firstpage :
800
Lastpage :
803
Abstract :
We propose a feature-based GVF snake for medical image segmentation here. Feature-based criteria are introduced for the GVF snake to stop its iterations. Without these criteria, the GVF snake might continue its iterations even though it has converged at the targeted object and result in longer computational time. The feature here is the area of the targeted object. Our proposed method comprises of two stages, namely the training stage and the segmentation stage. In the training stage, we acquire prior knowledge on the relative area of the targeted object from training data. In the segmentation stage, the proposed feature-based GVF snake is applied to segment the object from the image after computing the estimated area of the targeted object. In our proposed method, the GVF snake stops its iterations when the area bounded by its propagation is approximately equal to the estimated area and when it undergoes little change over two consecutive iterations. To illustrate the effectiveness of our proposed method, we applied it to the segmentation of the masseter muscle, which is the strongest jaw muscle, from 2-D magnetic resonance (MR) images. Numerical evaluation done indicates good agreement between the computerized and manual segmentations, with mean overlap of 92%.
Keywords :
biomedical MRI; feature extraction; gradient methods; image segmentation; medical image processing; muscle; vectors; 2-D magnetic resonance image; feature-based GVF snake; gradient vector flow; iteration; jaw muscle; masseter muscle; medical image segmentation; Biomedical engineering; Biomedical imaging; Deformable models; Head; Image converters; Image edge detection; Image segmentation; Medical diagnostic imaging; Muscles; Training data; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Masseter Muscle;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
ISSN :
1557-170X
Print_ISBN :
978-1-4244-0787-3
Type :
conf
DOI :
10.1109/IEMBS.2007.4352411
Filename :
4352411
Link To Document :
بازگشت