Joint Parametric and Non-parametric Curve Evolution for Medical Image Segmentation

Authors:
Mahshid Farzinfar;Zhong Xue;Eam Khwang Teoh
Affiliations:
School of EEE, Nanyang Technological University, Singapore, 639798;Department of Radiology, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA TX 77030;Department of Radiology, The Methodist Hospital and The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, USA TX 77030
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Year:
2008

Citing 12
Cited 1

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
Active shape models—their training and application

Computer Vision and Image Understanding
Geodesic Active Contours

International Journal of Computer Vision
Active Appearance Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Using Prior Shapes in Geometric Active Contours in a Variational Framework

International Journal of Computer Vision
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
A Variational Model for Object Segmentation Using Boundary Information and Shape Prior Driven by the Mumford-Shah Functional

International Journal of Computer Vision
Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation

International Journal of Computer Vision
Nonparametric shape priors for active contour-based image segmentation

Signal Processing
Prior Knowledge, Level Set Representations & Visual Grouping

International Journal of Computer Vision
Active contours without edges

IEEE Transactions on Image Processing

Organ Segmentation with Level Sets Using Local Shape and Appearance Priors

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new joint parametric and nonparametric curve evolution algorithm of the level set functions for medical image segmentation. Traditional level set algorithms employ non-parametric curve evolution for object matching. Although matching image boundaries accurately, they often suffer from local minima and generate incorrect segmentation of object shapes, especially for images with noise, occlusion and low contrast. On the other hand, statistical model-based segmentation methods allow parametric object shape variations subject to some shape prior constraints, and they are more robust in dealing with noise and low contrast. In this paper, we combine the advantages of both of these methods and jointly use parametric and non-parametric curve evolution in object matching. Our new joint curve evolution algorithm is as robust as and at the same time, yields more accurate segmentation results than the parametric methods using shape prior information. Comparative results on segmenting ventricle frontal horn and putamen shapes in MR brain images confirm both robustness and accuracy of the proposed joint curve evolution algorithm.