Abstract

We present a novel image contouring method based on the polarization features of the twisted-nematic liquid-crystal displays (TN-LCDs). TN-LCDs are manufactured to work between a crossed polarizer-analyzer pair. When the analyzer is at 45 deg (instead of 90 deg) with respect to the polarizer, one obtains an optically processed image with pronounced outlines (dark contours) at middle intensity, i.e., the borders between illuminated and dark areas are enhanced. The proposed method is quite robust and does not require precise alignment or coherent illumination. Since it does not involve numerical processing, it could be useful for contouring large images in real-time, which presents potential applications in medical and biological imaging. Validation experiments are presented.

© 2010 OSA

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2009

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

2008

C. S. Yelleswarapu, S. R. Kothapalli, and D. V. G. L. N. Rao, “Optical Fourier techniques for medical image processing and phase contrast imaging,” Opt. Commun. 281(7), 1876–1888 (2008).
[CrossRef] [PubMed]

2007

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

C. Greco, K. Rosenzweig, G. L. Cascini, and O. Tamburrini, “Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC),” Lung Cancer 57(2), 125–134 (2007).
[CrossRef] [PubMed]

G. Zhu, S. Zhang, Q. Zeng, C. Wang, Q. Zeng, and C. Wang, “Boundary-based image segmentation using binary level set method,” Opt. Eng. 46(5), 050501 (2007).
[CrossRef]

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

P. Phukpattaranont and P. Boonyaphiphat, “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images,” ECTI Trans. EEC 5, 158–164 (2007).

V. R. Korde, H. Bartels, J. Ranger-Moore, and J. Barton, “Automatic Segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Proc. SPIE 6633, 66330V (2007).
[CrossRef]

M. Ralló, M. S. Millán, and J. Escofet, “Referenceless segmentation of flaws in woven fabrics,” Appl. Opt. 46(27), 6688–6699 (2007).
[CrossRef] [PubMed]

2005

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

2004

2003

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
[CrossRef]

2001

2000

1999

O. Tsujii, M. T. Freedman, and S. K. Mun, “Lung contour detection in chest radiographs using 1-D convolution neural networks,” J. Electron. Imaging 8(1), 46–53 (1999).
[CrossRef]

1998

1988

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1(4), 321–331 (1988).
[CrossRef]

Abu-Baker, A.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Aykac, D.

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

Bartels, H.

V. R. Korde, H. Bartels, J. Ranger-Moore, and J. Barton, “Automatic Segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Proc. SPIE 6633, 66330V (2007).
[CrossRef]

Barton, J.

V. R. Korde, H. Bartels, J. Ranger-Moore, and J. Barton, “Automatic Segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Proc. SPIE 6633, 66330V (2007).
[CrossRef]

Bentzen, S. M.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Boersma, L.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Boonyaphiphat, P.

P. Phukpattaranont and P. Boonyaphiphat, “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images,” ECTI Trans. EEC 5, 158–164 (2007).

Bosmans, G.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Buijsen, J.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Carnielli, W. A.

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
[CrossRef]

Cartwright, C. M.

Cascini, G. L.

C. Greco, K. Rosenzweig, G. L. Cascini, and O. Tamburrini, “Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC),” Lung Cancer 57(2), 125–134 (2007).
[CrossRef] [PubMed]

Chan, Y. P.

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

Cheriet, M.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Chourey, K.

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

Cook, N. J.

Crabtree, K.

Davis, J. A.

De Ruysscher, D.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Dehing-Oberije, C.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Dekker, A.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Desautels, J. E. L.

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
[CrossRef]

Ding, M. S.

Escofet, J.

Evans, G. H.

Freedman, M. T.

O. Tsujii, M. T. Freedman, and S. K. Mun, “Lung contour detection in chest radiographs using 1-D convolution neural networks,” J. Electron. Imaging 8(1), 46–53 (1999).
[CrossRef]

Gillespie, W. A.

Gleason, S. S.

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

Greco, C.

C. Greco, K. Rosenzweig, G. L. Cascini, and O. Tamburrini, “Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC),” Lung Cancer 57(2), 125–134 (2007).
[CrossRef] [PubMed]

Guan, J.-H.

Guliato, D.

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
[CrossRef]

Guo, Y. P.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Hochstenbag, M.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Houben, R.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Kass, M.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1(4), 321–331 (1988).
[CrossRef]

Kharma, N.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Khoo, I. C.

Korde, V. R.

V. R. Korde, H. Bartels, J. Ranger-Moore, and J. Barton, “Automatic Segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Proc. SPIE 6633, 66330V (2007).
[CrossRef]

Kothapalli, S. R.

C. S. Yelleswarapu, S. R. Kothapalli, and D. V. G. L. N. Rao, “Optical Fourier techniques for medical image processing and phase contrast imaging,” Opt. Commun. 281(7), 1876–1888 (2008).
[CrossRef] [PubMed]

Kuo, S. W.

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

Laganiere, J.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Lambin, P.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

Liang, B. L.

Lin, H. H.

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

Liu, C.

Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

Liu, H. L.

Liu, Y.

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

Miao, Y.

Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

Millán, M. S.

Moghnieh, H.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Moreno, I.

Mu, G. G.

Mun, S. K.

O. Tsujii, M. T. Freedman, and S. K. Mun, “Lung contour detection in chest radiographs using 1-D convolution neural networks,” J. Electron. Imaging 8(1), 46–53 (1999).
[CrossRef]

Phukpattaranont, P.

P. Phukpattaranont and P. Boonyaphiphat, “Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images,” ECTI Trans. EEC 5, 158–164 (2007).

Price, J. R.

J. R. Price, D. Aykac, S. S. Gleason, K. Chourey, and Y. Liu, “Quantitative comparison of mitotic spindles by confocal image analysis,” J. Biomed. Opt. 10(4), 044012 (2005).
[CrossRef]

Ralló, M.

Rangayyan, R. M.

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
[CrossRef]

Ranger-Moore, J.

V. R. Korde, H. Bartels, J. Ranger-Moore, and J. Barton, “Automatic Segmentation of cell nuclei in bladder and skin tissue for karyometric analysis,” Proc. SPIE 6633, 66330V (2007).
[CrossRef]

Rao, D. V. G. L. N.

C. S. Yelleswarapu, S. R. Kothapalli, and D. V. G. L. N. Rao, “Optical Fourier techniques for medical image processing and phase contrast imaging,” Opt. Commun. 281(7), 1876–1888 (2008).
[CrossRef] [PubMed]

Rosenzweig, K.

C. Greco, K. Rosenzweig, G. L. Cascini, and O. Tamburrini, “Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC),” Lung Cancer 57(2), 125–134 (2007).
[CrossRef] [PubMed]

Rouleau, G.

N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

Shih, M. Y.

Shishido, A.

Shu, S. G.

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

Tamburrini, O.

C. Greco, K. Rosenzweig, G. L. Cascini, and O. Tamburrini, “Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC),” Lung Cancer 57(2), 125–134 (2007).
[CrossRef] [PubMed]

Terzopoulos, D.

M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: active contour models,” Int. J. Comput. Vis. 1(4), 321–331 (1988).
[CrossRef]

Tsujii, O.

O. Tsujii, M. T. Freedman, and S. K. Mun, “Lung contour detection in chest radiographs using 1-D convolution neural networks,” J. Electron. Imaging 8(1), 46–53 (1999).
[CrossRef]

van Baardwijk, A.

A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

van Kroonenburgh, M.

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[CrossRef] [PubMed]

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[CrossRef] [PubMed]

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A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
[CrossRef] [PubMed]

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H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
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Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

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[CrossRef]

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N. Kharma, H. Moghnieh, J. Yao, Y. P. Guo, A. Abu-Baker, J. Laganiere, G. Rouleau, and M. Cheriet, “Automatic segmentation of cells from microscopic imagery using ellipse detection,” IET Image Process. 1(1), 39–47 (2007).
[CrossRef]

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C. S. Yelleswarapu, S. R. Kothapalli, and D. V. G. L. N. Rao, “Optical Fourier techniques for medical image processing and phase contrast imaging,” Opt. Commun. 281(7), 1876–1888 (2008).
[CrossRef] [PubMed]

Yu, S. S.

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

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Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

G. Zhu, S. Zhang, Q. Zeng, C. Wang, Q. Zeng, and C. Wang, “Boundary-based image segmentation using binary level set method,” Opt. Eng. 46(5), 050501 (2007).
[CrossRef]

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[CrossRef]

Zhang, H.

Zhang, S.

G. Zhu, S. Zhang, Q. Zeng, C. Wang, Q. Zeng, and C. Wang, “Boundary-based image segmentation using binary level set method,” Opt. Eng. 46(5), 050501 (2007).
[CrossRef]

Zhu, G.

G. Zhu, S. Zhang, Q. Zeng, C. Wang, Q. Zeng, and C. Wang, “Boundary-based image segmentation using binary level set method,” Opt. Eng. 46(5), 050501 (2007).
[CrossRef]

Zuffo, J. A.

D. Guliato, R. M. Rangayyan, W. A. Carnielli, J. A. Zuffo, and J. E. L. Desautels, “Segmentation of breast tumors in mammograms using fuzzy sets,” J. Electron. Imaging 12(3), 369–378 (2003).
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IET Image Process.

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[CrossRef]

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[CrossRef]

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A. van Baardwijk, G. Bosmans, L. Boersma, J. Buijsen, S. Wanders, M. Hochstenbag, R. J. van Suylen, A. Dekker, C. Dehing-Oberije, R. Houben, S. M. Bentzen, M. van Kroonenburgh, P. Lambin, and D. De Ruysscher, “PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes,” Int. J. Radiat. Oncol. Biol. Phys. 68(3), 771–778 (2007).
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C. S. Yelleswarapu, S. R. Kothapalli, and D. V. G. L. N. Rao, “Optical Fourier techniques for medical image processing and phase contrast imaging,” Opt. Commun. 281(7), 1876–1888 (2008).
[CrossRef] [PubMed]

Opt. Eng.

Q. Zeng, Y. Miao, C. Liu, and S. Wang, “Algorithm based on marker-controlled watershed transform for overlapping plant fruit segmentation,” Opt. Eng. 48(2), 027201 (2009).
[CrossRef]

H. H. Lin, S. G. Shu, S. W. Kuo, C. H. Wang, Y. P. Chan, and S. S. Yu, “Alpha-gamma equalization-enhanced hand radiographic image segmentation scheme,” Opt. Eng. 48(10), 107001 (2009).
[CrossRef]

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[CrossRef]

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http://neurosurgery.ucla.edu/body.cfm?id=178

http://www.bio.davidson.edu/misc/movies/mitosislily.mov

Supplementary Material (1)

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Figures (4)

Fig. 1
Fig. 1

Proposed setup.

Fig. 2
Fig. 2

(a) Partial view of the USAF1951 pattern with additive Gaussian noise. (b)-(d) Optically processed images with I B increasing in steps of 0.2 between consecutive images.

Fig. 3
Fig. 3

Brain MRI image. The left-side shows the original image, while the right-side shows the optically processed image with contoured tumor.

Fig. 4
Fig. 4

(Media 1) Cell division of Haemantus katherinae observed with phase contrast microscopy. The left-side shows the original image, while the right-side shows the optically processed image with contoured chromosomes.

Equations (6)

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I ( x , y ) = cos 2 ( θ ( x , y ) ) ,
I o u t ( x , y ) = cos 2 ( θ ( x , y ) + ξ )                 = ( 1 / 2 ) { 1 + cos ( 2 θ ( x , y ) )     cos ( 2 ξ ) sin ( 2 θ ( x , y ) )     sin ( 2 ξ ) } .
I o u t ( x , y ) = ( 1 / 2 ) { 1 + ( 2 I ( x , y ) 1 ) cos ( 2 ξ ) 1 ( 2 I ( x , y ) 1 ) 2 sin ( 2 ξ ) } .
I o u t ( x , y ) = 1 1 ( 2 I ( x , y ) 1 ) 2 .
I o u t ( x , y ) = 1 1 [ 2 ( I ( x , y ) + I B ) 1 ] 2 .
I o u t ( x , y ) τ = 2 [ 2 ( I ( x , y ) + I B ) 1 ] 1 [ 2 ( I ( x , y ) + I B ) 1 ] 2 I ( x , y ) τ

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