Abstract

We demonstrate a technique to improve structural data obtained from Optical Projection Tomography (OPT) using Image Fusion (IF) and contrast normalization. This enables the visualization of molecular expression patterns in biological specimens with highly variable contrast values. In the approach, termed IF-OPT, different exposures are fused by assigning weighted contrasts to each. When applied to projection images from mouse organs and digital phantoms our results demonstrate the capability of IF-OPT to reveal high and low signal intensity details in challenging specimens. We further provide measurements to highlight the benefits of the new algorithm in comparison to other similar methods.

© 2013 OSA

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2013 (1)

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

2012 (2)

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

P. Fei, Z. Yu, X. Wang, P. J. Lu, Y. Fu, Z. He, J. Xiong, and Y. Huang, “High dynamic range optical projection tomography (HDR-OPT),” Opt. Express20(8), 8824–8836 (2012).
[CrossRef] [PubMed]

2011 (1)

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3(4), 204–208 (2011).
[CrossRef] [PubMed]

2010 (2)

S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Inf. Fusion11(2), 114–123 (2010).
[CrossRef]

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

2009 (3)

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: a simple and practical alternative to high dynamic range photograph,” Comput. Graph. Forum28(1), 161–171 (2009).
[CrossRef]

D. Looney and D. P. Mandic, “Multi-scale image fusion using complex extensions of EMD,” IEEE Trans. Signal Process.57(4), 1626–1630 (2009).
[CrossRef]

C.-J. Du and D.-W. Sun, “Retrospective shading correction of confocal laser canning microscopy beef images for three-dimensional visualization,” Food Bioprocess Tech2(2), 167–176 (2009).
[CrossRef]

2008 (2)

Z. B. Wang and Y. Ma, “Medical image fusion using m-PCNN,” Inf. Fusion9(2), 176–185 (2008).
[CrossRef]

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

2007 (4)

N. Mitianoudis and T. Stathaki, “Pixel-based and region-based image fusion schemes using ICA bases,” Inf. Fusion8(2), 131–142 (2007).
[CrossRef]

A. O. Akyuz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J Vis Commun Image R18(5), 366–376 (2007).
[CrossRef]

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

2005 (2)

A. A. Goshtasby, “Fusion of multi-exposure images,” Image Vis. Comput.23(6), 611–618 (2005).
[CrossRef]

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol.50(19), 4645–4665 (2005).
[CrossRef] [PubMed]

2002 (2)

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

1999 (1)

Z. Zhang and R. S. Blum, “A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application,” Proc. IEEE87(8), 1315–1326 (1999).
[CrossRef]

1998 (1)

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

1994 (2)

K. Stark, S. Vainio, G. Vassileva, and A. P. McMahon, “Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4,” Nature372(6507), 679–683 (1994).
[CrossRef] [PubMed]

G. K. Matsopoulos, S. Marshall, and J. N. H. Brunt, “Multiresolution morphological fusion of MR and CT images of the human brain,” IEEE Proc.Vis Image Sign141(3), 137–142 (1994).

1990 (1)

A. Toet, “Hierarchical image fusion,” Mach. Vis. Appl.3(1), 1–11 (1990).
[CrossRef]

1987 (1)

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

1984 (1)

S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr.8(2), 300–305 (1984).
[PubMed]

Ahlgren, U.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3(4), 204–208 (2011).
[CrossRef] [PubMed]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Akyuz, A. O.

A. O. Akyuz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J Vis Commun Image R18(5), 366–376 (2007).
[CrossRef]

Alanentalo, T.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

Amburn, E. P.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Asayesh, A.

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

Austin, J. D.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Baldock, R.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Bayoumi, M.

M. Ghantous, S. Ghosh, and M. Bayoumi, “A gradient-based hybrid image fusion scheme using object extraction,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 2008), pp. 1300–1303.
[CrossRef]

Blum, R. S.

Z. Zhang and R. S. Blum, “A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application,” Proc. IEEE87(8), 1315–1326 (1999).
[CrossRef]

Braeuning, M. P.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Brunt, J. N. H.

G. K. Matsopoulos, S. Marshall, and J. N. H. Brunt, “Multiresolution morphological fusion of MR and CT images of the human brain,” IEEE Proc.Vis Image Sign141(3), 137–142 (1994).

Bruzzone, L.

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

Bull, D. R.

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

S. G. Nikolov, D. R. Bull, C. N. Canagarajah, M. Halliwell, and P. N. T. Wells, “Image fusion using a 3-D wavelet transform,” in Proceedings of IEEE Conference on Image Processing And Its Applications.(IEEE, 1999), pp. 235–239.
[CrossRef]

Burt, P. J.

P. J. Burt and R. J. Kolczynski, “Enhanced image capture through fusion,” in Proceedings of IEEE Conference on Computer Vision. (IEEE, 1993), pp. 173–182.

Canagarajah, C. N.

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

S. G. Nikolov, D. R. Bull, C. N. Canagarajah, M. Halliwell, and P. N. T. Wells, “Image fusion using a 3-D wavelet transform,” in Proceedings of IEEE Conference on Image Processing And Its Applications.(IEEE, 1999), pp. 235–239.
[CrossRef]

Cheddad, A.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3(4), 204–208 (2011).
[CrossRef] [PubMed]

Cheng, S.

S. Cheng, J. He, and Z. Lv, “Medical image of PET/CT weighted fusion based on wavelet transform,” in Proceedings of IEEE Conference on Bioinformatics and Biomedical Engineering, (IEEE, 2008), pp. 2523–2525.
[CrossRef]

Chipman, L. J.

L. J. Chipman, T. M. Orr, and L. N. Graham, “Wavelets and image fusion,” in Proceedings of IEEE Conference on Image Processing. (IEEE, 1995), pp. 248–251.
[CrossRef]

Cromartie, R.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Daneshvar, S.

S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Inf. Fusion11(2), 114–123 (2010).
[CrossRef]

Davidson, D.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

DeLuca, M.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Du, C.-J.

C.-J. Du and D.-W. Sun, “Retrospective shading correction of confocal laser canning microscopy beef images for three-dimensional visualization,” Food Bioprocess Tech2(2), 167–176 (2009).
[CrossRef]

Eriksson, A. U.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

Eriksson, M.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

Farina, A.

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

Fei, P.

Fu, Y.

Georgsson, F.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

Geselowitz, A.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Ghantous, M.

M. Ghantous, S. Ghosh, and M. Bayoumi, “A gradient-based hybrid image fusion scheme using object extraction,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 2008), pp. 1300–1303.
[CrossRef]

Ghassemian, H.

S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Inf. Fusion11(2), 114–123 (2010).
[CrossRef]

Ghosh, S.

M. Ghantous, S. Ghosh, and M. Bayoumi, “A gradient-based hybrid image fusion scheme using object extraction,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 2008), pp. 1300–1303.
[CrossRef]

Goshtasby, A. A.

A. A. Goshtasby, “Fusion of multi-exposure images,” Image Vis. Comput.23(6), 611–618 (2005).
[CrossRef]

Graham, L. N.

L. J. Chipman, T. M. Orr, and L. N. Graham, “Wavelets and image fusion,” in Proceedings of IEEE Conference on Image Processing. (IEEE, 1995), pp. 248–251.
[CrossRef]

Greer, T.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Halliwell, M.

S. G. Nikolov, D. R. Bull, C. N. Canagarajah, M. Halliwell, and P. N. T. Wells, “Image fusion using a 3-D wavelet transform,” in Proceedings of IEEE Conference on Image Processing And Its Applications.(IEEE, 1999), pp. 235–239.
[CrossRef]

He, J.

S. Cheng, J. He, and Z. Lv, “Medical image of PET/CT weighted fusion based on wavelet transform,” in Proceedings of IEEE Conference on Bioinformatics and Biomedical Engineering, (IEEE, 2008), pp. 2523–2525.
[CrossRef]

He, Z.

Hecksher-Sørensen, J.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Hemminger, B. M.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Henkelman, R. M.

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol.50(19), 4645–4665 (2005).
[CrossRef] [PubMed]

Hill, B.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Holmberg, D.

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

Hong-Zhi, X.

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

Hörnblad, A.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3(4), 204–208 (2011).
[CrossRef] [PubMed]

Huang, S.

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

Huang, Y.

Hui, L.

L. Hui, B. S. Manjunath, and S. K. Mitra, “Multi-sensor image fusion using the wavelet transform,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 1994), pp. 51–55.
[CrossRef]

Jing-Wen, Y.

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

Johnston, R. E.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Kautz, J.

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: a simple and practical alternative to high dynamic range photograph,” Comput. Graph. Forum28(1), 161–171 (2009).
[CrossRef]

Kolczynski, R. J.

P. J. Burt and R. J. Kolczynski, “Enhanced image capture through fusion,” in Proceedings of IEEE Conference on Computer Vision. (IEEE, 1993), pp. 173–182.

Kostromina, E.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

Lewis, J. J.

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

Looney, D.

D. Looney and D. P. Mandic, “Multi-scale image fusion using complex extensions of EMD,” IEEE Trans. Signal Process.57(4), 1626–1630 (2009).
[CrossRef]

Lorén, C. E.

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

Lu, P. J.

Lv, Z.

S. Cheng, J. He, and Z. Lv, “Medical image of PET/CT weighted fusion based on wavelet transform,” in Proceedings of IEEE Conference on Bioinformatics and Biomedical Engineering, (IEEE, 2008), pp. 2523–2525.
[CrossRef]

Ma, Y.

Z. B. Wang and Y. Ma, “Medical image fusion using m-PCNN,” Inf. Fusion9(2), 176–185 (2008).
[CrossRef]

Mandic, D. P.

D. Looney and D. P. Mandic, “Multi-scale image fusion using complex extensions of EMD,” IEEE Trans. Signal Process.57(4), 1626–1630 (2009).
[CrossRef]

Manjunath, B. S.

L. Hui, B. S. Manjunath, and S. K. Mitra, “Multi-sensor image fusion using the wavelet transform,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 1994), pp. 51–55.
[CrossRef]

Marshall, S.

G. K. Matsopoulos, S. Marshall, and J. N. H. Brunt, “Multiresolution morphological fusion of MR and CT images of the human brain,” IEEE Proc.Vis Image Sign141(3), 137–142 (1994).

Matsopoulos, G. K.

G. K. Matsopoulos, S. Marshall, and J. N. H. Brunt, “Multiresolution morphological fusion of MR and CT images of the human brain,” IEEE Proc.Vis Image Sign141(3), 137–142 (1994).

McMahon, A. P.

K. Stark, S. Vainio, G. Vassileva, and A. P. McMahon, “Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4,” Nature372(6507), 679–683 (1994).
[CrossRef] [PubMed]

Mertens, T.

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: a simple and practical alternative to high dynamic range photograph,” Comput. Graph. Forum28(1), 161–171 (2009).
[CrossRef]

Mitianoudis, N.

N. Mitianoudis and T. Stathaki, “Pixel-based and region-based image fusion schemes using ICA bases,” Inf. Fusion8(2), 131–142 (2007).
[CrossRef]

Mitra, S. K.

L. Hui, B. S. Manjunath, and S. K. Mitra, “Multi-sensor image fusion using the wavelet transform,” in Proceedings of IEEE Conference on Image Processing, (IEEE, 1994), pp. 51–55.
[CrossRef]

Morabito, F. C.

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

Morrison, H.

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

Muller, K.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Nikolov, S. G.

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

S. G. Nikolov, D. R. Bull, C. N. Canagarajah, M. Halliwell, and P. N. T. Wells, “Image fusion using a 3-D wavelet transform,” in Proceedings of IEEE Conference on Image Processing And Its Applications.(IEEE, 1999), pp. 235–239.
[CrossRef]

Norlin, N.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

O'Callaghan, R. J.

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

Orr, T. M.

L. J. Chipman, T. M. Orr, and L. N. Graham, “Wavelets and image fusion,” in Proceedings of IEEE Conference on Image Processing. (IEEE, 1995), pp. 248–251.
[CrossRef]

Park, D. S.

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

Perry, P.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Pileggi, A.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

Pisano, E. D.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Pizer, S. M.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr.8(2), 300–305 (1984).
[PubMed]

Rao, N.

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

Reinhard, E.

A. O. Akyuz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J Vis Commun Image R18(5), 366–376 (2007).
[CrossRef]

Ross, A.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Serpico, S. B.

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

Sharpe, J.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4(1), 31–33 (2007).
[CrossRef] [PubMed]

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol.50(19), 4645–4665 (2005).
[CrossRef] [PubMed]

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3D microscopy and gene expression studies,” Science296(5567), 541–545 (2002).
[CrossRef] [PubMed]

Simone, G.

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

Sled, J. G.

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol.50(19), 4645–4665 (2005).
[CrossRef] [PubMed]

Staab, E. V.

S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr.8(2), 300–305 (1984).
[PubMed]

Stark, K.

K. Stark, S. Vainio, G. Vassileva, and A. P. McMahon, “Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4,” Nature372(6507), 679–683 (1994).
[CrossRef] [PubMed]

Stathaki, T.

N. Mitianoudis and T. Stathaki, “Pixel-based and region-based image fusion schemes using ICA bases,” Inf. Fusion8(2), 131–142 (2007).
[CrossRef]

Sun, D.-W.

C.-J. Du and D.-W. Sun, “Retrospective shading correction of confocal laser canning microscopy beef images for three-dimensional visualization,” Food Bioprocess Tech2(2), 167–176 (2009).
[CrossRef]

Svensson, C.

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
[PubMed]

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

ter Haar Romeny, B.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
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Toet, A.

A. Toet, “Hierarchical image fusion,” Mach. Vis. Appl.3(1), 1–11 (1990).
[CrossRef]

Vainio, S.

K. Stark, S. Vainio, G. Vassileva, and A. P. McMahon, “Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4,” Nature372(6507), 679–683 (1994).
[CrossRef] [PubMed]

Van Reeth, F.

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: a simple and practical alternative to high dynamic range photograph,” Comput. Graph. Forum28(1), 161–171 (2009).
[CrossRef]

Vassileva, G.

K. Stark, S. Vainio, G. Vassileva, and A. P. McMahon, “Epithelial transformation of metanephric mesenchyme in the developing kidney regulated by Wnt-4,” Nature372(6507), 679–683 (1994).
[CrossRef] [PubMed]

Walls, J. R.

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol.50(19), 4645–4665 (2005).
[CrossRef] [PubMed]

Wang, X.

Wang, Z. B.

Z. B. Wang and Y. Ma, “Medical image fusion using m-PCNN,” Inf. Fusion9(2), 176–185 (2008).
[CrossRef]

Wells, P. N. T.

S. G. Nikolov, D. R. Bull, C. N. Canagarajah, M. Halliwell, and P. N. T. Wells, “Image fusion using a 3-D wavelet transform,” in Proceedings of IEEE Conference on Image Processing And Its Applications.(IEEE, 1999), pp. 235–239.
[CrossRef]

Xiao-Bo, Q.

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

Xiong, J.

Yang, Y.

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

Yu, Z.

Zhang, Z.

Z. Zhang and R. S. Blum, “A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application,” Proc. IEEE87(8), 1315–1326 (1999).
[CrossRef]

Zimmerman, J. B.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr.8(2), 300–305 (1984).
[PubMed]

Zi-Qian, Z.

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

Zong, S.

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

Zuiderveld, K.

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Acta Automatica Sinica (1)

Q. Xiao-Bo, Y. Jing-Wen, X. Hong-Zhi, and Z. Zi-Qian, “Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain,” Acta Automatica Sinica34, 1508–1514 (2008).

Comput Vision Graph (1)

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, and K. Zuiderveld, “Adaptive histogram equalization and its variations,” Comput Vision Graph39(3), 355–368 (1987).
[CrossRef]

Comput. Graph. Forum (1)

T. Mertens, J. Kautz, and F. Van Reeth, “Exposure fusion: a simple and practical alternative to high dynamic range photograph,” Comput. Graph. Forum28(1), 161–171 (2009).
[CrossRef]

EURASIP J. Adv. Signal Process. (1)

Y. Yang, D. S. Park, S. Huang, and N. Rao, “Medical image fusion via an effective wavelet-based approach,” EURASIP J. Adv. Signal Process.2010, 1–14 (2010).
[CrossRef]

Food Bioprocess Tech (1)

C.-J. Du and D.-W. Sun, “Retrospective shading correction of confocal laser canning microscopy beef images for three-dimensional visualization,” Food Bioprocess Tech2(2), 167–176 (2009).
[CrossRef]

IEEE Proc.Vis Image Sign (1)

G. K. Matsopoulos, S. Marshall, and J. N. H. Brunt, “Multiresolution morphological fusion of MR and CT images of the human brain,” IEEE Proc.Vis Image Sign141(3), 137–142 (1994).

IEEE Trans. Med. Imaging (1)

A. Cheddad, C. Svensson, J. Sharpe, F. Georgsson, and U. Ahlgren, “Image processing assisted algorithms for optical projection tomography,” IEEE Trans. Med. Imaging31(1), 1–15 (2012).
[CrossRef] [PubMed]

IEEE Trans. Signal Process. (1)

D. Looney and D. P. Mandic, “Multi-scale image fusion using complex extensions of EMD,” IEEE Trans. Signal Process.57(4), 1626–1630 (2009).
[CrossRef]

Image Vis. Comput. (1)

A. A. Goshtasby, “Fusion of multi-exposure images,” Image Vis. Comput.23(6), 611–618 (2005).
[CrossRef]

Inf. Fusion (5)

Z. B. Wang and Y. Ma, “Medical image fusion using m-PCNN,” Inf. Fusion9(2), 176–185 (2008).
[CrossRef]

S. Daneshvar and H. Ghassemian, “MRI and PET image fusion by combining IHS and retina-inspired models,” Inf. Fusion11(2), 114–123 (2010).
[CrossRef]

G. Simone, A. Farina, F. C. Morabito, S. B. Serpico, and L. Bruzzone, “Image fusion techniques for remote sensing applications,” Inf. Fusion3(1), 3–15 (2002).
[CrossRef]

N. Mitianoudis and T. Stathaki, “Pixel-based and region-based image fusion schemes using ICA bases,” Inf. Fusion8(2), 131–142 (2007).
[CrossRef]

J. J. Lewis, R. J. O'Callaghan, S. G. Nikolov, D. R. Bull, and C. N. Canagarajah, “Pixel- and region-based image fusion with complex wavelets,” Inf. Fusion8(2), 119–130 (2007).
[CrossRef]

Islets (1)

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3(4), 204–208 (2011).
[CrossRef] [PubMed]

J Vis Commun Image R (1)

A. O. Akyuz and E. Reinhard, “Noise reduction in high dynamic range imaging,” J Vis Commun Image R18(5), 366–376 (2007).
[CrossRef]

J. Comput. Assist. Tomogr. (1)

S. M. Pizer, J. B. Zimmerman, and E. V. Staab, “Adaptive grey level assignment in CT scan display,” J. Comput. Assist. Tomogr.8(2), 300–305 (1984).
[PubMed]

J. Digit. Imaging (1)

E. D. Pisano, S. Zong, B. M. Hemminger, M. DeLuca, R. E. Johnston, K. Muller, M. P. Braeuning, and S. M. Pizer, “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” J. Digit. Imaging11(4), 193–200 (1998).
[CrossRef] [PubMed]

J. Vis. Exp. (1)

A. U. Eriksson, C. Svensson, A. Hörnblad, A. Cheddad, E. Kostromina, M. Eriksson, N. Norlin, A. Pileggi, J. Sharpe, F. Georgsson, T. Alanentalo, and U. Ahlgren, “Near infrared Optical projection tomography for assessments of β-cell mass distribution in diabetes research,” J. Vis. Exp. (71): e50238 (2013).
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Figures (10)

Fig. 1
Fig. 1

Depiction of the principle way to set up the three different exposures for IF-OPT (liver specimen). This is for mere illustration; the actual scanner displays one plot at a time. Initially, the specimen is rotated to locate the brightest area where the scanner displays a saturation plot. For the “normal” exposure the plot should be close to the over saturation level but not exceeding it whereas the high exposure should allow for certain parts of the specimen to be over-saturated (the purpose here is to catch all weak signals at the expense of over-saturating other areas) and finally the low exposure should be at or below the middle range. Note, these superimposed plots form a general guideline and saturation may vary depending on the examined specimen and the level of staining.

Fig. 2
Fig. 2

A generic illustration of the mechanics underlying CLAHE. (Top) showing the two major intensity transformation involved, T1, a local adaptive histogram equalization with a clip-limit (will be discussed later) and T2, a bilinear interpolation which removes the blocking effect by interpolating between adjacent blocks’ boundaries, and (bottom) showing the interaction formed by CLAHE with two different tile sizes as in Eqs. (1)-(2).

Fig. 3
Fig. 3

A simplified illustration of the effect of the compound histogram equalization and redistribution on three different exposures (16-bit) corresponding to the images shown in Fig. 6. Intensity histograms of the three different exposures are depicted, namely, (a) I0 (low exposure, fine details are not well captured), (b) I1 (best adjusted exposure, a mild improvement in capturing the fine details but at the expense of an equivalent oversaturation) and (c) I2 (high exposure, a great improvement in capturing the details however more pixels are oversaturated), (d) the histogram after applying Eq. (1) on the fused projection of the above three exposures, and (e) after applying Eq. (2) on the result of Eq. (1). As can be seen from the final result in Fig. 6, the main advantages have been achieved; the process exposes very fine details without being trapped in the oversaturated area.

Fig. 4
Fig. 4

Structural analysis of a digital phantom. (a)-(d), Frames from the digital phantom showing three different exposures, (low (a), normal (b) and high (c)) together with the proposed fusion method (d). The “screw-like” topology of the phantom was intentionally introduced to help contrast the efficiency of the proposed technique with the finest tuned exposure in terms of object structure enhancement. Note that the diameter of the tube and the “ring structures” were made constant across the length of the object. (e) and (f) depict 3D rendering of reconstructions of (b) and (d). The proposed fusion reveals low intensity regions (without over-saturating other areas) and aids in shape preservation. For example, in the IF-OPT reconstruction the diameter of the tubes is more consistent across its length (black arrows in (e) and (f)). Moreover, the ring structures at the bottom of the phantom are deformed (open arrowheads in (e) and (f)) and the uppermost ring structures are essentially absent after 3D reconstruction (black arrowheads in (e) and (f)). “Oversaturated” areas in (a) -(c) are shown in Fig. 5.

Fig. 5
Fig. 5

Depiction of over-saturated areas in the digital phantom shown in Fig. 4(a)-4(c), Individual projection views corresponding to low (a), normal (b) and high (c) exposure. (d)-(e), Over-saturated areas in the respective projection view.

Fig. 6
Fig. 6

Comparative assessment of the effect of IF-OPT versus normal single exposure settings. (a)-(c), Different exposures of a projection at angle 0° of an Adult C57BL/6 (Taconic) left lateral liver lobe (lobus sinister lateralis) labeled for smooth muscle α-actin and (d), the result of applying the proposed IF-OPT method. (a) Depicts the first projection of the low (500ms) single-exposure scan, (b) the first projection of the normal (1500ms) single-exposure, (c) the first projection of the high (2500ms) single-exposure, and (d) the first projection of the IF-OPT fused-exposures. A major gain in signal capture is noticeable when using IF-OPT. (d), (e) 3D reconstruction of the normal exposure (e) and IF-OPT (f) projection data shown in (a)-(d). The clear perceptual advantage of IF-OPT pinpoints the usefulness of the technique in weak signal enhancement, structure highlights and intensity regulation across the specimen (which also facilitates thresholding during reconstruction). Arrowhead in (b) indicates the oversaturated area. Scalebar in (a) corresponds to 1.8mm in (a)–(f).

Fig. 7
Fig. 7

IF-OPT overcomes the trade off in threshold settings for comparative analysis of the ureteric kidney tree in a neonatal Wnt4 null mouse (a)-(f) and its wild type littermate (g), (h). (a)and (e) Volume renderings of normal and IF-OPT reconstructions. (b)-(d), (f), Corresponding iso-surfaces reconstructions using three different thresholds (b)-(d) and a IF-OPT iso-surface reconstruction using a medium threshold (f). (g) shows a IF-OPT iso-surface reconstruction of the normal kidney whereas (h) shows an iso-surface reconstruction of same kidney based on a normal exposure. The branching epithelial network was stained with antibodies against Troma-I and visualized by a Alexa594 secondary antibody. Scalebar in (e) corresponds to 100µm in (a)–(f) and scalebar in (g) corresponds to 200μm in (g) and (h).

Fig. 8
Fig. 8

Comparison of anatomical structures in a mouse liver lobe (vessels labeled for smooth muscle α-actin) using different fusing methods on the exposures shown in Fig. 6. A HVS score of all methods ranked the IF-OPT method (h) as the one providing most detail of the vascular network (for details see Table 1.). Scale bar in (a) corresponds to 1.8mm in (a)-(j). For the details of the methods see section 3.4.

Fig. 9
Fig. 9

Effect of IF-OPT using an alternative camera. The images depict volume renderings of an adult C57BL/6 (Taconic) left lateral liver lobe (lobus sinister lateralis) labeled for smooth muscle α-actin. Images were acquired using an Andor iKon-M DU934N-BV 16bit CCD camera fitted to the OPT scanner setup described by Sharpe et al [1, 30]. This camera has a considerably higher dynamic range compared to the camera delivered with the Bioptonics 3001 scanner used in Figs. 6-8 of this report. (a) depicts a single, normal, exposure (8000ms) image and (b) an IF-OPT image comprised of three exposures/ projection, low (1500ms), normal (8000ms) and high (15000ms). Scalebar in (b) corresponds to 2mm in (a) and (b).

Fig. 10
Fig. 10

Comparison of alternative use of the components of the IF-OPT approach. The images depict volume renderings of an adult C57BL/6 (Taconic) left lateral liver lobe (lobus sinister lateralis) labeled for smooth muscle α-actin. The images illustrate: (a) “Normal” OPT (same as in Fig. 6(b)), (b) the effect of applying CLAHE to the projection images prior to reconstruction, (c) the effect of applying CLAHE to the 2D tomographic data of the fused projection images and (d) the effect of IF-OPT as described in section 3.2. For blinded HVS ranking of (a) - (d), see Table 2. Scale bar in (a) corresponds to 1.8mm in (a)-(d).

Tables (2)

Tables Icon

Table 1 Comprehensive data from HVS ranking of images in Fig. 8

Tables Icon

Table 2 Comprehensive data from HVS ranking of images in Fig. 10.

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

I ^ = i=0 2 ω i h ¯ ( I iC ) .
h ¯ ( I 0 ) ω Average , h ¯ ( I 1 ) ω Max , h ¯ ( I 2 ) ω Min .
h ¯ ¯ ( I ^ )= I ^ min + 2 δ 2 ln( 1 1ρ( f ^ ) ) .
Cli p Limit = φ 256 +[ 0.01( φ φ 256 ) ].

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