Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational level set approach to multiphase motion
Journal of Computational Physics
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
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Statistical Approach to Snakes for Bimodal and Trimodal Imagery
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of neighboring organs in medical image with model competition
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Image Processing
Level Set Segmentation With Multiple Regions
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
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In this work, we present an active contour scheme to simultaneously extract multiple targets from MR and CT medical imagery. A number of previous active contour methods are capable of only extracting one object at a time. Therefore, when multiple objects are required, the segmentation process must be performed sequentially. Not only may this be tedious work, but moreover the relationship between the given objects is not addressed in a uniform framework, making the method prone to leakage and overlap among the individual segmentation results. On the other hand, many of the algorithms providing the capability to perform simultaneous multiple object segmentation, tacitly or explicitly assume that the union of the multiple regions equals the whole image domain. However, this is often invalid for many medical imaging tasks. In the present work, we give a straightforward methodology to alleviate these drawbacks as follows. First, local robust statistics are used to describe the object features, which are learned adaptively from user provided seeds. Second, several active contours evolve simultaneously with their interactions being governed by simple principles derived from mechanics. This not only guarantees mutual exclusiveness among the contours, but also no longer relies upon the assumption that the multiple objects fill the whole image domain. In doing so, the contours interact and converge to equilibrium at the desired positions of the given objects. The method naturally handles the issues of leakage and overlapping. Both qualitative and quantitative results are shown to highlight the algorithm's capability of extracting several targets as well as robustly preventing the leakage.