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Proceedings Paper

Three-band MRI image fusion utilizing the wavelet-based method optimized with two quantitative fusion metrics
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Paper Abstract

In magnetic resonance imaging (MRI), there are three bands of images ("MRI triplet") available, which are T1-, T2- and PD-weighted images. The three images of a MRI triplet provide complementary structure information and therefore it is useful for diagnosis and subsequent analysis to combine three-band images into one. We propose an advanced discrete wavelet transform (αDWT) for three-band MRI image fusion and the αDWT algorithm is further optimized utilizing two quantitative fusion metrics - the image quality index (IQI) and ratio spatial frequency error (rSFe). In the aDWT method, principle component analysis (PCA) and morphological processing are incorporated into a regular DWT fusion algorithm. Furthermore, the αDWT has two adjustable parameters - the level of DWT decomposition (Ld) and the length of the selected wavelet (Lw) that determinately affect the fusion result. The fused image quality can be quantitatively measured with the established metrics - IQI and rSFe. Varying the control parameters (Ld and Lw), an iterative fusion procedure can be implemented and running until an optimized fusion is achieved. We fused and analyzed several MRI triplets from the Visible Human Project® female dataset. From the quantitative and qualitative evaluations of fused images, we found that (1) the αDWTi-IQI algorithm produces a smoothed image whereas the αDWTi-rSFe algorithm yields a sharpened image, (2) fused image "T1+T2" is the most informative one in comparison with other two-in-one fusions (PD+T1 and PD+T2), (3) for three-in-one fusions, no significant difference is observed among the three fusions of (PD+T1)+T2, (PD+T2)+T1 and (T1+T2)+PD, thus the order of fusion does not play an important role. The fused images can significantly benefit medical diagnosis and also the further image processing such as multi-modality image fusion (with CT images), visualization (colorization), segmentation, classification and computer-aided diagnosis (CAD).

Paper Details

Date Published: 10 March 2006
PDF: 12 pages
Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 61440R (10 March 2006); doi: 10.1117/12.651689
Show Author Affiliations
Yufeng Zheng, Univ. of Louisville (United States)
Adel S. Elmaghraby, Univ. of Louisville (United States)
Hichem Frigui, Univ. of Louisville (United States)

Published in SPIE Proceedings Vol. 6144:
Medical Imaging 2006: Image Processing
Joseph M. Reinhardt; Josien P. W. Pluim, Editor(s)

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