Advanced algorithmic approaches to medical image segmentation
Boundary extraction in thermal images by edge map
Proceedings of the 2004 ACM symposium on Applied computing
Journal of Computational Physics
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D statistical models for tooth surface reconstruction
Computers in Biology and Medicine
Pattern Recognition Letters
Medical Image Segmentation Based on the Bayesian Level Set Method
Medical Imaging and Informatics
Shape recovery by a generalized topology preserving SOM
Neurocomputing
Smooth 3-D Reconstruction for 2-D Histological Images
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
An Efficient Morphological Active Surface Model for Volumetric Image Segmentation
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Model-based quantitative AAA image analysis using a priori knowledge
Computer Methods and Programs in Biomedicine
Variational B-spline level-set: a linear filtering approach for fast deformable model evolution
IEEE Transactions on Image Processing
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Textural image segmentation using discrete cosine transform
CIT'09 Proceedings of the 3rd International Conference on Communications and information technology
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
A unified tensor level set for image segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A level set method based on the Bayesian risk for medical image segmentation
Pattern Recognition
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation
Computers in Biology and Medicine
Segmentation of image using watershed and fast level set methods
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Pattern Recognition Letters
Mathematical morphology in computer graphics, scientific visualization and visual exploration
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Segmentation of the surfaces of the retinal layer from OCT images
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A physically-motivated deformable model based on fluid dynamics
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A segmentation and reconstruction technique for 3d vascular structures
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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
A generalized level set formulation of the mumford-shah functional for brain MR image segmentation
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Applying prior knowledge in the segmentation of 3d complex anatomic structures
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
The persistent morse complex segmentation of a 3-manifold
3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
Expert Systems with Applications: An International Journal
Multi-agent stochastic level set method in image segmentation
Computer Vision and Image Understanding
Pattern Recognition and Image Analysis
Multi-object segmentation approach based on topological derivative and level set method
Integrated Computer-Aided Engineering
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The class of geometric deformable models, also known as level sets, has brought tremendous impact to medical imagery due to its capability of topology preservation and fast shape recovery. In an effort to facilitate a clear and full understanding of these powerful state-of-the-art applied mathematical tools, the paper is an attempt to explore these geometric methods, their implementations and integration of regularizers to improve the robustness of these topologically independent propagating curves/surfaces. The paper first presents the origination of level sets, followed by the taxonomy of level sets. We then derive the fundamental equation of curve/surface evolution and zero-level curves/surfaces. The paper then focuses on the first core class of level sets, known as "level sets without regularizers." This class presents five prototypes: gradient, edge, area-minimization, curvature-dependent and application driven. The next section is devoted to second core class of level sets, known as "level sets with regularizers." In this class, we present four kinds: clustering-based, Bayesian bidirectional classifier-based, shape-based and coupled constrained-based. An entire section is dedicated to optimization and quantification techniques for shape recovery when used in the level set framework. Finally, the paper concludes with 22 general merits and four demerits on level sets and the future of level sets in medical image segmentation. We present applications of level sets to complex shapes like the human cortex acquired via MRI for neurological image analysis.