Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A logic filter for tumor detection on mammograms
Journal of Computer Science and Technology
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
Computers in Biology and Medicine
Level set image segmentation with Bayesian analysis
Neurocomputing
Gradient Vector Flow Field and Mass Region Extraction in Digital Mammograms
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scene segmentation based on IPCA for visual surveillance
Neurocomputing
The Gabor-Based Tensor Level Set Method for Multiregional Image Segmentation
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Embedded Geometric Active Contour with Shape Constraint for Mass Segmentation
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Image quality assessment based on multiscale geometric analysis
IEEE Transactions on Image Processing
A review of active appearance models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
IEEE Transactions on Information Technology in Biomedicine
A Relay Level Set Method for Automatic Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Optimized graph-based segmentation for ultrasound images
Neurocomputing
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Mammographic mass segmentation plays an important role in computer-aided diagnosis systems. It is very challenging because masses are always of low contrast with ambiguous margins, connected with the normal tissues, and of various scales and complex shapes. To effectively detect true boundaries of mass regions, we propose a feature embedded vector-valued contour-based level set method with relaxed shape constraint. In particular, we initially use the contour-based level set method to obtain the initial boundaries on the smoothed mammogram as the shape constraint. To prevent the contour leaking and meanwhile preserve the radiative characteristics of specific malignant masses, afterward, we relax the obtained shape constraint by analyzing possible valid regions around the initial boundaries. The relaxed shape constraint is then used to design a novel stopping function for subsequent vector-valued level set method. Since texture maps, gradient maps, and the original intensity map can reflect different characteristics of the mammogram, we integrate them together to obtain more accurate segmentation by incorporating the new stopping function into the newly proposed feature embedded vector-valued contour-based level set method. The experimental results suggest that the proposed feature embedded vector-valued contour-based level set method with relaxed shape constraint can effectively find ambiguous margins of the mass regions. Comparing against existing active contours methods, the new scheme is more effective and robust in detecting complex masses.