A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association
Estimation of noise in images: an evaluation
CVGIP: Graphical Models and Image Processing
Digital image processing algorithms
Digital image processing algorithms
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Level-Set Approach to 3D Reconstruction from Range Data
International Journal of Computer Vision
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Extraction of Carotid Artery Contours from Ultrasound Images
CBMS '00 Proceedings of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00)
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties
IEEE Transactions on Signal Processing
Original Articles: Time-scale energy based analysis of contours of real-world shapes
Mathematics and Computers in Simulation
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Medical image segmentation is a sufficiently complex problem that no single strategy has proven to be completely effective. Historically, region growing, clustering, and edge tracing have been used and while significant steps have been made in the first two, research into automatic, recursive, boundary following has not kept pace. A new, advanced, edge-tracing algorithm capable of combining edge, region, and pixel-classification information, and suitable for magnetic resonance image analysis, is described. The algorithm is inspired by automatic target tracking, as used in civilian and military aerospace operations. Comparison with clustering and level sets is performed. Results indicate that no method is uniformly superior, that the new algorithm provides information not available from the other approaches, and that it can utilize a variety of sources including results from other methods. The algorithm is applied to two-dimensional slice images and extension to three-dimensional images is discussed.