Title :
Regularized shape deformation for image segmentation
Author :
Wang, Song ; Liang, Zhi-Pei
Author_Institution :
Beckman Inst., Illinois Univ., Urbana, IL, USA
Abstract :
This paper presents a new method for image segmentation by deforming the object shape in a template. The deformation process is controlled using a thin-plate spline kernel based regularization method. The proposed method is especially useful for 2D-based segmentation of 3D medical images by treating segmented slices as templates for their neighboring unsegmented slices. We have applied the proposed method to extract the scalp contours in brain cryosection images with very encouraging results
Keywords :
brain; cryogenics; feature extraction; image segmentation; medical image processing; 2D-based segmentation; 3D medical images; brain cryosection images; image segmentation; object shape deformation; regularized shape deformation; scalp contour extraction; templates; thin-plate spline kernel based regularization; Active contours; Biomedical imaging; Covariance matrix; Deformable models; Image segmentation; Kernel; Medical treatment; Process control; Shape; Spline;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
Conference_Location :
Salt Lake City, UT
Print_ISBN :
0-7803-7041-4
DOI :
10.1109/ICASSP.2001.941233
