Paper Info
Title
Generic Method For Intensity Standardization Of Medical Images Using Multiscale Curvelet Representation
Abstract
Most computer-aided diagnosis (CAD) methods in medical imaging are finely tuned for the settings of training data, i.e., the acquisition protocol and machine settings. Therefore, they may fail to perform optimally on images acquired under a different protocol. Intensity standardization, or mapping the acquired data to a predefined intensity profile, can alleviate this challenge. In this work, we present a generic method for intensity standardization of 2D/3D medical images using localized subband energy scaling of the multi-scale curvelet transform. During the training phase, reference data are first decomposed into scale and orientation localized subbands using the multiscale curvelet transform, followed by calculating a reference energy value for each subband. During the testing stage, the localized energy of each subband is scaled to the reference localized energy value from the training stage through an iterative process. We validated our generic standardization method on 2D chest Xrays (CXR) and 3D T1-weighted MRI sequences acquired using different scanners on a group of both healthy and diseased subjects. A significant improvement (Dice coefficient of 0.91 +/- 0.05 versus 0.68 +/- 0.13, p-value<0.001) was obtained in the whole brain segmentation accuracy after standardization. Similarly, for air-trapping quantification, the standardization improved the correlation with the expert visual assessment of air-trapping from CXR from R=0.32 to R=0.93. The proposed intensity standardization technique could be adopted as a pre-processing step for 2D and 3D data to improve the accuracy of CAD on data obtained from variable sources.
Year
DOI
Venue
2016
10.1109/ISBI.2016.7493510
2016 IEEE 13TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI)
Field
DocType
ISSN
Brain segmentation,Reference data (financial markets),CAD,Computer vision,Pattern recognition,Iterative and incremental development,Medical imaging,Computer science,Sørensen–Dice coefficient,Artificial intelligence,Standardization,Curvelet
Conference
1945-7928
Citations 
PageRank 
References 
1
0.35
8
Authors
2
Name
Order
Citations
PageRank
Awais Mansoor16812.49
Marius George Linguraru236248.94