On the Incorporation of shape priors into geometric active contours

Authors:
Yunmei Chen;Sheshadri Thiruvenkadam;Feng Huang;David Wilson;Edward A. Geiser; Md;Hemant D. Tagare
Affiliations:
-;-;-;-;-;-;-
Venue:
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Year:
2001

Citing 0
Cited 32

Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation

International Journal of Computer Vision
Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford-Shah Functional

International Journal of Computer Vision
Using Prior Shapes in Geometric Active Contours in a Variational Framework

International Journal of Computer Vision
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis

International Journal of Computer Vision
Shape Priors for Level Set Representations

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Combinative Multi-scale Level Set Framework for Echocardiographic Image Segmentation

MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Higher Order Active Contours

International Journal of Computer Vision
Incorporating shape prior into geodesic active contours for detecting partially occluded object

Pattern Recognition
Higher-Order Active Contour Energies for Gap Closure

Journal of Mathematical Imaging and Vision
Review: A comparative study of deformable contour methods on medical image segmentation

Image and Vision Computing
Prior Knowledge, Level Set Representations & Visual Grouping

International Journal of Computer Vision
Regularized Reconstruction of Shapes with Statistical a priori Knowledge

International Journal of Computer Vision
A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother

SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Simplex Mesh Diffusion Snakes: Integrating 2D and 3D Deformable Models and Statistical Shape Knowledge in a Variational Framework

International Journal of Computer Vision
Robust Medical Images Segmentation Using Learned Shape and Appearance Models

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Towards recognition-based variational segmentation using shape priors and dynamic labeling

Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
LV segmentation through the analysis of radio frequency ultrasonic images

IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Graph cuts framework for kidney segmentation with prior shape constraints

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Active contours in optical flow fields for image sequence segmentation

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A multiresolution support vector machine based algorithm for pneumoconiosis detection from chest radiographs

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A model-based approach to the segmentation of nasal cavity and paranasal sinus boundaries

Proceedings of the 32nd DAGM conference on Pattern recognition
Automated interventricular septum thickness measurement from B-mode echocardiograms

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
2D and 3D shape based segmentation using deformable models

MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Shape prior embedded geodesic distance transform for image segmentation

ACCV'10 Proceedings of the 2010 international conference on Computer vision - Volume part II
A combined segmentation and registration framework with a nonlinear elasticity smoother

Computer Vision and Image Understanding
Spatio-temporal prior shape constraint for level set segmentation

EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Active shape models and segmentation of the left ventricle in echocardiography

Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
A statistical level set framework for segmentation of left ventricle

IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Robust segmentation for left ventricle based on curve evolution

KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Spaces and manifolds of shapes in computer vision: An overview

Image and Vision Computing
A survey of graph theoretical approaches to image segmentation

Pattern Recognition
Ultrasound kidney segmentation with a global prior shape

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel model for boundary determination that incorporates prior shape information into geometric active contours is presented. The basic idea of this model is to minimize the energy functional depending on the information of the image gradient and the shape of interest, so that the boundary of the object can be captured either by higher magnitude of the image gradient or by the prior knowledge of its shape. The level set form of the proposed model is also provided. We present our experimental results on some synthetic images, functional MR brain images, and ultrasound images for which the existing active contours methods are not applicable. The existence of the solution to the proposed minimization problem is also discussed.