MR contrast synthesis for lesion segmentation

Authors:
Snehashis Roy;Aaron Carass;Navid Shiee;Dzung L. Pham;Jerry L. Prince
Affiliations:
Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University;Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University;Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University;MedIC, Neuroradiology Division, Radiology and Radiological Science, The Johns Hopkins University;Image Analysis and Communications Laboratory, Electrical and Computer Engineering, The Johns Hopkins University
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 5
Cited 0

Example-Based Super-Resolution

IEEE Computer Graphics and Applications
A Non-Local Algorithm for Image Denoising

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Bayesian MS Lesion Classification Modeling Regional and Local Spatial Information

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Model-based segmentation of multimodal images

CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
An information fidelity criterion for image quality assessment using natural scene statistics

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The magnetic resonance contrast of a neuroimaging data set has strong impact on the utility of the data in image analysis tasks, such as registration and segmentation. Lengthy acquisition times often prevent routine acquisition of multiple MR contrast images, and opportunities for detailed analysis using these data would seem to be irrevocably lost. This paper describes an example based approach which uses patch matching from a multiple contrast atlas with the intended goal of generating an alternate MR contrast image, thus effectively simulating alternative pulse sequences from one another. In this paper, we deal specifically with Fluid Attenuated Inversion Recovery (FLAIR) sequence generation from Tl and T2 pulse sequences. The applicability of this synthetic FLAIR for estimating white matter lesions segmentation is demonstrated.