Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR

Authors:
M.-P. Jolly;C. Alvino;B. Odry;X. Deng;J. Zheng;M. Harder;J. Guehring
Affiliations:
Siemens Corporate Research, Imaging and Visualization Department, Princeton, NJ;Siemens Corporate Research, Imaging and Visualization Department, Princeton, NJ;Siemens Corporate Research, Imaging and Visualization Department, Princeton, NJ;Corporate Technology, Siemens Ltd. China, P. R. China;Corporate Technology, Siemens Ltd. China, P. R. China;Siemens Healthcare, MR Division, Erlangen, Germany;Siemens Corporate Research, Imaging and Visualization Department, Princeton, NJ
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 3
Cited 1

Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation

Graphical Models and Image Processing
Multiseeded Segmentation Using Fuzzy Connectedness

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random Walks for Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Auto-alignment of knee MR scout scans through redundant, adaptive and hierarchical anatomy detection

IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an automatic algorithm to extract the knee frame of reference from 3D MR isotropic scans. The method ultimately seeks to determine two lines that are tangent to the bottom of the condyles in an axial and a coronal plane. It consists of three major parts, initial detection of the knee joint using Hidden Markov Models, femur segmentation using Random Walker segmentation, and finally condyle detection. We demonstrate on 30 datasets that our algorithm is very robust and performs at the same level as a human reader.