Name
Title | ||
|---|---|---|
Image fusion methodology for efficient interpretation of multiband images in 3D high-resolution ultrasonic transmission tomography |
Abstract | ||
|---|---|---|
With the critical innovations of using submillimeter transducers and multiband analysis of the first arrival pulse, a high-resolution ultrasonic transmission tomography (HUTT) system has been built and tested to produce multiband images of biological organs at submillimeter resolution. Since the resulting multiband images consist of frequency-dependent attenuation coefficients (relative to water reference) of transmitted ultrasound pulses, their contrast and sharpness depend on the specific frequency band(s) used for image formation. Even though this multiband representation provides a powerful tool for soft-tissue differentiation, it hinders visual inspection and limits the visual interpretation of image contents in a short time. To facilitate the visual interpretation of HUTT multiband images, this article presents an efficient image fusion methodology called local principal component analysis with structure tensor (LPCA-ST). The LPCA has been known as a feasible tool for the fusion of spectral data, since it utilizes the principal components of spectral data as a fusion-weighting vector of local area. Nonetheless, the LPCA-fused image often suffers from oversmoothness because of the redundancy of the spectral data. To prevent this problem, we propose a structure tensor as the metric used to select the most informative bands for subsequent LPCA fusion. Our preliminary studies have shown that the contrast of the LPCA-fused image improves dramatically only when multiband images whose values of the respective structure tensor are the highest are used in the LPCA fusion process. This is achieved in 3D without increasing the computational complexity of the fusion process. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 277–282, 2009 |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1002/ima.v19:4 | Int. J. Imaging Systems and Technology |
Keywords | Field | DocType |
image fusion,structure tensor,high resolution | Computer vision,Image fusion,Frequency band,Computer science,Image formation,Tomography,Redundancy (engineering),Structure tensor,Artificial intelligence,Principal component analysis,Computational complexity theory | Journal |
Volume | Issue | ISSN |
19 | 4 | 0899-9457 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeong-Won Jeong | 1 | 17 | 5.64 |
| Dae C Shin | 2 | 24 | 5.57 |
| vasilis z marmarelis | 3 | 219 | 29.17 |