On active contour models and balloons
CVGIP: Image Understanding
Compact Object Recognition Using Energy-Function-Based Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Approach to Dynamic Contours Through Stochastic Sampling and Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sectored Snakes: Evaluating Learned-Energy Segmentations
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Area and length minimizing flows for shape segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Segmentation and border identification of cells in images of peripheral blood smear slides
ACSC '07 Proceedings of the thirtieth Australasian conference on Computer science - Volume 62
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Blood cell identification and segmentation by means of statistical models
ISCGAV'07 Proceedings of the 7th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Integrating local distribution information with level set for boundary extraction
Journal of Visual Communication and Image Representation
Continuous force field analysis for generalized gradient vector flow field
Pattern Recognition
Shape context for image understanding
SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Histology image analysis for carcinoma detection and grading
Computer Methods and Programs in Biomedicine
Journal of Visual Communication and Image Representation
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In this paper, a class of deformable contour methods using a constrained optimization approach of minimizing a contour energy function satisfying an interior homogeneity constraint is proposed. The class is defined by any positive potential function describing the contour interior characterization. An evolutionary strategy is used to derive the algorithm. A similarity threshold Tv can be used to determine the interior size and shape of the contour. Sensitivity and significance of Tv and σ (a spreadness measure) are also discussed and shown. Experiments on noisy images and the convergence to a minimum energy gap contour are included. The developed method has been applied to a variety of medical images from CT abdominal section, MRI image slices of brain, brain tumor, a pig heart ultrasound image sequence to visual blood cell images. As the results show, the algorithm can be adapted to a broad range of medical images containing objects with vague, complex and/or irregular shape boundary, inhomogeneous and noisy interior, and contour with small gaps.