Title
Deformable boundary finding in medical images by integrating gradient and region information
Abstract
Accurately segmenting and quantifying structures is a key issue in biomedical image analysis. The two conventional methods of image segmentation, region-based segmentation, and boundary finding, often suffer from a variety of limitations. Here the authors propose a method which endeavors to integrate the two approaches in an effort to form a unified approach that is robust to noise and poor initialization. The authors' approach uses Green's theorem to derive the boundary of a homogeneous region-classified area in the image and integrates this with a gray level gradient-based boundary finder. This combines the perceptual notions of edge/shape information with gray level homogeneity. A number of experiments were performed both on synthetic and real medical images of the brain and heart to evaluate the new approach, and it is shown that the integrated method typically performs better when compared to conventional gradient-based deformable boundary finding. Further, this method yields these improvements with little increase in computational overhead, an advantage derived from the application of the Green's theorem.
Year
DOI
Venue
1996
10.1109/42.544503
Medical Imaging, IEEE Transactions
Keywords
Field
DocType
Green's function methods,brain,cardiology,edge detection,image segmentation,medical image processing,Green's theorem,biomedical image analysis,brain images,computational overhead,deformable boundary finding,gray level gradient-based boundary finder,heart images,homogeneous region-classified area,medical diagnostic imaging,region information,region-based segmentation,synthetic images
Overhead (computing),Computer vision,Green's function,Homogeneity (statistics),Segmentation,Edge detection,Image processing,Image segmentation,Artificial intelligence,Initialization,Mathematics
Journal
Volume
Issue
ISSN
15
6
0278-0062
Citations 
PageRank 
References 
158
16.73
25
Authors
3
Search Limit
100158
Name
Order
Citations
PageRank
Chakraborty, A.116618.07
Lawrence H Staib21129159.56
Duncan, J.S.315816.73