MR image segmentation of the knee bone using phase information-Reference-Cited by-同舟云学术

MR image segmentation of the knee bone using phase information

Published:2007-08 Issue:4 Volume:11 Page:325-335
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Bourgeat Pierrick,Fripp Jurgen,Stanwell Peter,Ramadan Saadallah,Ourselin Sébastien

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference32 articles.

1. The use of unwrapped phase in MR image segmentation: a preliminary study;Bourgeat,2005

2. Features extraction on complex images;Bourgeat,2004

3. Bourgeat, P., Stanwell, P., Ramadan, S., Ourselin, S., 2006. Texture-based segmentation of the knee bones in MRI using phase information. In: ISBI’06. Arlington, VA, pp. 742–745.

4. Multichannel texture analysis using localized spatial filters;Bovik;IEEE Transactions on Pattern Analysis and Machine Intelligence,1990

5. Texture analysis of medical images;Castellano;Clinical Radiology,2004

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