Hairline Fracture Detection using MRF and Gibbs Sampling

Detection of hairline fractures, representing points or areas of discontinuity in the bone, is a clinically challenging task, especially in presence of noise. The above problem is equally appealing from a computer vision or pattern recognition perspective since (a) traditional techniques for detecti...

Full description

Saved in:
Bibliographic Details
Published in2007 IEEE Workshop on Applications of Computer Vision (WACV '07) p. 56
Main Authors Chowdhury, A.S., Bhattacharya, A., Bhandarkar, S.M., Datta, G.S., Yu, J.C., Figueroa, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2007
Subjects
Bones
Computer vision
Degradation
Humans
Markov random fields
Noise robustness
Pattern recognition
Probability
Sampling methods
Surface cracks
Online AccessGet full text

Cover

More Information
Summary:Detection of hairline fractures, representing points or areas of discontinuity in the bone, is a clinically challenging task, especially in presence of noise. The above problem is equally appealing from a computer vision or pattern recognition perspective since (a) traditional techniques for detection of corners, denoting points of surface discontinuity, typically fail in such cases and, (b) one needs to implicitly handle unknown local degradation in the image. A novel two-phase scheme for hairline mandibular fracture detection, that is robust to noise, is proposed. In the first phase, the hairline fractures are coarsely localized using statistical correlation and by exploiting the bilateral symmetry of the human mandible. In the second phase, the fractures are precisely identified and highlighted using a Markov random field (MRF) modeling approach coupled with maximum a posteriori probability (MAP) estimation. Gibbs sampling is used to maximize the posterior probability. Experimental results on computer tomography (CT) scans from real patients are presented
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2007.28