Unsupervised 3D prostate segmentation based on diffusion-weighted imaging MRI using active contour models with a shape prior

Authors:
Xin Liu;Masoom A. Haider;Imam Samil Yetik
Affiliations:
Department of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL;Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Toronto, ON, Canada;Department of Electrical and Computer Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL
Venue:
Journal of Electrical and Computer Engineering
Year:
2011

Citing 11
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
Recovery of Parametric Models from Range Images: The Case for Superquadrics with Global Deformations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based strategies for active contour models

International Journal of Computer Vision
Fitting Superellipses

IEEE Transactions on Pattern Analysis and Machine Intelligence
Level Set Based Shape Prior Segmentation

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Motion segmentation and retrieval for 3D video based on modified shape distribution

EURASIP Journal on Applied Signal Processing
Automatic target recognition in synthetic aperture sonar images based on geometrical feature extraction

EURASIP Journal on Advances in Signal Processing
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
A novel 3D segmentation approach for segmenting the prostate from dynamic contrast enhanced MRI using current appearance and learned shape prior

ISSPIT '10 Proceedings of the The 10th IEEE International Symposium on Signal Processing and Information Technology
Prostate Segmentation with Texture Enhanced Active Appearance Model

SITIS '10 Proceedings of the 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems
Active contours without edges

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurate estimation of the prostate location and volume from in vivo images plays a crucial role in various clinical applications. Recently, magnetic resonance imaging (MRI) is proposed as a promising modality to detect and monitor prostate-related diseases. In this paper, we propose an unsupervised algorithm to segment prostate with 3D apparent diffusion coefficient (ADC) images derived from diffusion-weighted imaging (DWI) MRI without the need of a training dataset, whereas previous methods for this purpose require training datasets. We first apply a coarse segmentation to extract the shape information. Then, the shape prior is incorporated into the active contour model. Finally, morphological operations are applied to refine the segmentation results. We apply our method to an MR dataset obtained from three patients and provide segmentation results obtained by our method and an expert. Our experimental results show that the performance of the proposed method is quite successful.