Abstract

Colposcopy is a primary diagnostic method used to detect cancer and precancerous lesions of the uterine cervix. During the examination, the metaplastic and abnormal tissues exhibit different degrees of whiteness (acetowhitening effect) after applying a 3%–5% acetic acid solution. Colposcopists evaluate the color and density of the acetowhite tissue to assess the severity of lesions for the purpose of diagnosis, telemedicine, and annotation. However, the color and illumination of the colposcopic images vary with the light sources, the instruments and camera settings, as well as the clinical environments. This makes assessment of the color information very challenging even for an expert. In terms of developing a Computer-Aided Diagnosis (CAD) system for colposcopy, these variations affect the performance of the feature extraction algorithm for the acetowhite color. Non-uniform illumination from the light source is also an obstacle for detecting acetowhite regions, lesion margins, and anatomic features. There fore, in digital colposcopy, it is critical to map the color appearance of the images taken with different colposcopes into one standard color space with normalized illumination. This paper presents a novel image calibration technique for colposcopic images. First, a specially designed calibration unit is mounted on the colposcope to acquire daily calibration data prior to performing subject examinations. The calibration routine is fast, automated, accurate and reliable. We then use our illumination correction algorithm and a color calibration algorithm to calibrate the exam data. In this paper we describe these techniques and demonstrate their applications in clinical studies.

© 2006 Optical Society of America

Full Article  |  PDF Article
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References

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    [Crossref]
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    [Crossref]
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    [Crossref] [PubMed]
  23. A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
    [Crossref] [PubMed]
  24. W. Li, STI® Medical Systems, 733 Bishop Street, Honolulu, Hawaii 96813, is preparing a manuscript to be called “Acetowhite color feature extraction algorithm for cervical images.”

2006 (1)

W. Li and A. Poisson, “Detection and characterization of abnormal vascular patterns in automated cervical image analysis,” Lecture Notes in Computer Science : Advances in Visual Computing 4292, 627–636 (2006).
[Crossref]

2003 (2)

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

2001 (1)

G. Paschos, “Perceptually uniform color spaces for color texture analysis: an empirical evaluation,” IEEE Trans. Image Process. 10, 932–936 (2001).
[Crossref]

2000 (2)

H. C. Li, “Regularized color clustering in medical image database,” IEEE Trans. Med. Imaging 19, 1150–1155 (2000).
[Crossref]

Q. Ji, J. Engel, and E. Craine, “Texture Analysis for Classification of Cervix Lesions,” IEEE Trans. Med. Imaging 19, 1144–1149 (2000).
[Crossref]

1993 (1)

B. L. Craine and E. R. Craine, “Digital imaging colposcopy: basic concepts and applications,” Obstet. Gynecol. 82, 869–873 (1993).
[PubMed]

1992 (1)

M. S. Mikhail, I. R. Merkatz, and S. L. Romney, “Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia,” Obstet. Gynecol. 80, 5–8 (1992).
[PubMed]

1985 (1)

R. Reid and P. Scalzi, “Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia,” Am. J. Obstet. Gynecol. 153, 611–618 (1985).
[PubMed]

Antani, S.

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

Atkinson, E. N.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Benavides, J. M.

Benedet, J. L.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Boulanger, J-C.

I. Claude, R. Winzenrieth, P. Pouletaut, and J-C. Boulanger, “Contour Features for Colposcopic Images Classification by Artificial Neural Networks,” in Proceedings of International Conference on Pattern Recognition, 771–774 (2002).

Chang, S.

Claude, I.

I. Claude, R. Winzenrieth, P. Pouletaut, and J-C. Boulanger, “Contour Features for Colposcopic Images Classification by Artificial Neural Networks,” in Proceedings of International Conference on Pattern Recognition, 771–774 (2002).

Cox, J. T.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

Craine, B. L.

B. L. Craine and E. R. Craine, “Digital imaging colposcopy: basic concepts and applications,” Obstet. Gynecol. 82, 869–873 (1993).
[PubMed]

Craine, E.

Q. Ji, J. Engel, and E. Craine, “Texture Analysis for Classification of Cervix Lesions,” IEEE Trans. Med. Imaging 19, 1144–1149 (2000).
[Crossref]

Craine, E. R.

B. L. Craine and E. R. Craine, “Digital imaging colposcopy: basic concepts and applications,” Obstet. Gynecol. 82, 869–873 (1993).
[PubMed]

Ehlen, T.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Engel, J.

Q. Ji, J. Engel, and E. Craine, “Texture Analysis for Classification of Cervix Lesions,” IEEE Trans. Med. Imaging 19, 1144–1149 (2000).
[Crossref]

Ferris, D.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

Ferris, D. G.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

Foerster, J.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

Follen, M.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Gordon, S.

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

S. Gordon, G. Zimmerman, and H. Greenspan, “Image Segmentation of Uterine Cervix Images for Indexing in PACs,” in Proceedings of IEEE 17th Symposium on Computer-based Medical Systems (2004).

Greenspan, H.

S. Gordon, G. Zimmerman, and H. Greenspan, “Image Segmentation of Uterine Cervix Images for Indexing in PACs,” in Proceedings of IEEE 17th Symposium on Computer-based Medical Systems (2004).

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

Gu, J.

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

Guo, J.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Hadad, N.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Hakansson, J.

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

Hansson, U.

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

Hernes, D.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Iakovidis, D. K.

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

Jeronimo, J.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

Ji, Q.

Q. Ji, J. Engel, and E. Craine, “Texture Analysis for Classification of Cervix Lesions,” IEEE Trans. Med. Imaging 19, 1144–1149 (2000).
[Crossref]

Karkanis, S. A.

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

Karras, D. A.

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

King, P.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Lange, H.

V. Van Raad, Z. Xue, and H. Lange, “Lesion margin analysis for automated classification of cervical cancer lesions,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144 (2006).
[Crossref]

H. Lange, “Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix,” Image Processing, J. M Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 1004–1017 (2005).
[Crossref]

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

Li, H. C.

H. C. Li, “Regularized color clustering in medical image database,” IEEE Trans. Med. Imaging 19, 1150–1155 (2000).
[Crossref]

Li, W.

W. Li and A. Poisson, “Detection and characterization of abnormal vascular patterns in automated cervical image analysis,” Lecture Notes in Computer Science : Advances in Visual Computing 4292, 627–636 (2006).
[Crossref]

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

W. Li, STI® Medical Systems, 733 Bishop Street, Honolulu, Hawaii 96813, is preparing a manuscript to be called “Acetowhite color feature extraction algorithm for cervical images.”

Long, R.

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

MacAulay, C.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Mackinnon, N.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Malpica, A.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Maroulis, D. E.

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

Matisic, J.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Merkatz, I. R.

M. S. Mikhail, I. R. Merkatz, and S. L. Romney, “Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia,” Obstet. Gynecol. 80, 5–8 (1992).
[PubMed]

Mikhail, M. S.

M. S. Mikhail, I. R. Merkatz, and S. L. Romney, “Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia,” Obstet. Gynecol. 80, 5–8 (1992).
[PubMed]

Milbourne, A.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Miller, D.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Mitra, S.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Nutter, B.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

O’Connor, D. M.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

Palus, H.

H. Palus, Colour spaces (Chapmann and Hall, 1998).

Park, S. Y.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Paschos, G.

G. Paschos, “Perceptually uniform color spaces for color texture analysis: an empirical evaluation,” IEEE Trans. Image Process. 10, 932–936 (2001).
[Crossref]

Phillips, B.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Poisson, A.

W. Li and A. Poisson, “Detection and characterization of abnormal vascular patterns in automated cervical image analysis,” Lecture Notes in Computer Science : Advances in Visual Computing 4292, 627–636 (2006).
[Crossref]

Pouletaut, P.

I. Claude, R. Winzenrieth, P. Pouletaut, and J-C. Boulanger, “Contour Features for Colposcopic Images Classification by Artificial Neural Networks,” in Proceedings of International Conference on Pattern Recognition, 771–774 (2002).

Reid, R.

R. Reid and P. Scalzi, “Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia,” Am. J. Obstet. Gynecol. 153, 611–618 (1985).
[PubMed]

Rhodes, H.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Richards-Kortum, R.

J. M. Benavides, S. Chang, S. Y. Park, R. Richards-Kortum, N. Mackinnon, C. MacAulay, A. Milbourne, A. Malpica, and M. Follen, “Multispectral digital colposcopy for in vivo detection of cervical cancer,” Opt. Express 11, 1223–1236 (2003).
[Crossref] [PubMed]

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Romney, S. L.

M. S. Mikhail, I. R. Merkatz, and S. L. Romney, “Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia,” Obstet. Gynecol. 80, 5–8 (1992).
[PubMed]

Scalzi, P.

R. Reid and P. Scalzi, “Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia,” Am. J. Obstet. Gynecol. 153, 611–618 (1985).
[PubMed]

Schiffman, M.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Srinivasan, Y.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Sriraja, Y.

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Styles, W. S.

G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New York: Wiley, 1982).

Tulpule, B.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Tzivras, M.

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

Van, Niekirk D.

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

Van Raad, V.

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

V. Van Raad, Z. Xue, and H. Lange, “Lesion margin analysis for automated classification of cervical cancer lesions,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144 (2006).
[Crossref]

Winzenrieth, R.

I. Claude, R. Winzenrieth, P. Pouletaut, and J-C. Boulanger, “Contour Features for Colposcopic Images Classification by Artificial Neural Networks,” in Proceedings of International Conference on Pattern Recognition, 771–774 (2002).

Wolf, S

S Wolf, is preparing a manuscript to be called “Color Correction Matrix for Digital Still and Video Imaging Systems.”

Wright, V. C.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

Wyszecki, G.

G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New York: Wiley, 1982).

Xue, Z.

V. Van Raad, Z. Xue, and H. Lange, “Lesion margin analysis for automated classification of cervical cancer lesions,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144 (2006).
[Crossref]

Yagneswaran, S.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

Yang, S.

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Zimmerman, G.

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

S. Gordon, G. Zimmerman, and H. Greenspan, “Image Segmentation of Uterine Cervix Images for Indexing in PACs,” in Proceedings of IEEE 17th Symposium on Computer-based Medical Systems (2004).

Am. J. Obstet. Gynecol. (1)

R. Reid and P. Scalzi, “Genital warts and cervical cancer. VII. An improved colposcopic index for differentiating benign papillomaviral infections from high-grade cervical intraepithelial neoplasia,” Am. J. Obstet. Gynecol. 153, 611–618 (1985).
[PubMed]

IEEE Trans. Image Process. (1)

G. Paschos, “Perceptually uniform color spaces for color texture analysis: an empirical evaluation,” IEEE Trans. Image Process. 10, 932–936 (2001).
[Crossref]

IEEE Trans. Inf. Technol. Biomed. (1)

S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Trans. Inf. Technol. Biomed. 7, 141–152 (2003).
[Crossref] [PubMed]

IEEE Trans. Med. Imaging (2)

H. C. Li, “Regularized color clustering in medical image database,” IEEE Trans. Med. Imaging 19, 1150–1155 (2000).
[Crossref]

Q. Ji, J. Engel, and E. Craine, “Texture Analysis for Classification of Cervix Lesions,” IEEE Trans. Med. Imaging 19, 1144–1149 (2000).
[Crossref]

Lecture Notes in Computer Science : Advances in Visual Computing (1)

W. Li and A. Poisson, “Detection and characterization of abnormal vascular patterns in automated cervical image analysis,” Lecture Notes in Computer Science : Advances in Visual Computing 4292, 627–636 (2006).
[Crossref]

Obstet. Gynecol. (2)

B. L. Craine and E. R. Craine, “Digital imaging colposcopy: basic concepts and applications,” Obstet. Gynecol. 82, 869–873 (1993).
[PubMed]

M. S. Mikhail, I. R. Merkatz, and S. L. Romney, “Clinical usefulness of computerized colposcopy: image analysis and conservative management of mild dysplasia,” Obstet. Gynecol. 80, 5–8 (1992).
[PubMed]

Opt. Express (1)

Other (15)

A. Milbourne, S. Y. Park, J. L. Benedet, D. Miller, T. Ehlen, H. Rhodes, A. Malpica, J. Matisic, Niekirk D. Van, E. N. Atkinson, N. Hadad, N. Mackinnon, C. MacAulay, R. Richards-Kortum, and M. Follen, “Results of a pilot study of multispectral digital colposcopy for the in vivo detection of cervical intraepithelial neoplasia,” Gynecol. Oncol. (2005).
[Crossref] [PubMed]

W. Li, STI® Medical Systems, 733 Bishop Street, Honolulu, Hawaii 96813, is preparing a manuscript to be called “Acetowhite color feature extraction algorithm for cervical images.”

S. Gordon, G. Zimmerman, and H. Greenspan, “Image Segmentation of Uterine Cervix Images for Indexing in PACs,” in Proceedings of IEEE 17th Symposium on Computer-based Medical Systems (2004).

H. Lange, “Automatic detection of multi-level acetowhite regions in RGB color images of the uterine cervix,” Image Processing, J. M Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 1004–1017 (2005).
[Crossref]

S. Gordon, G. Zimmerman, R. Long, S. Antani, J. Jeronimo, and H. Greenspan, “Content analysis of uterine cervix images: initial steps towards content based indexing and retrieval of cervigrams,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144, 1549–1556 (2006).

I. Claude, R. Winzenrieth, P. Pouletaut, and J-C. Boulanger, “Contour Features for Colposcopic Images Classification by Artificial Neural Networks,” in Proceedings of International Conference on Pattern Recognition, 771–774 (2002).

V. Van Raad, Z. Xue, and H. Lange, “Lesion margin analysis for automated classification of cervical cancer lesions,” Image Processing, J. M. Reinhardt and J. P. Pluim, eds., in Proc. SPIE6144 (2006).
[Crossref]

W. Li, V. Van Raad, J. Gu, U. Hansson, J. Hakansson, H. Lange, and D. Ferris, “Computer-aided Diagnosis (CAD) for cervical cancer screening and diagnosis: a new system design in medical image processing,” Lecture Notes in Computer Science, CVBIA 2005240–250 (2005).
[Crossref]

International agency for research in cancer, “Globocan 2002 database,” http://www-dep.iarc.fr/.

D. G. Ferris, J. T. Cox, D. M. O’Connor, V. C. Wright, and J. Foerster, Modern Colposcopy. Textbook and Atlas (American Society for Colposcopy and Cervical Pathology, 2004).

S. Yang, J. Guo, P. King, Y. Sriraja, S. Mitra, B. Nutter, D. Ferris, M. Schiffman, J. Jeronimo, and R. Long, “A multi-spectral digital cervigram™ analyzer in the wavelet domain for early detection of cervical cancer,” Image Processing, J. M. Fitzpatrick and M. Sonka, eds., in Proc. SPIE5370, 1833–1844 (2004).
[Crossref]

Y. Srinivasan, D. Hernes, B. Tulpule, S. Yang, J. Guo, S. Mitra, S. Yagneswaran, B. Nutter, B. Phillips, R. Long, and D. Ferris, “A probabilistic approach to segmentation and classification of neoplasia in uterine cervix images using color and geometric features,” Image Processing, J. M. Fitzpatrick and J. M. Reinhardt, eds., in Proc. SPIE5747, 995–1003 (2005).
[Crossref]

H. Palus, Colour spaces (Chapmann and Hall, 1998).

G. Wyszecki and W. S. Styles, Color Science: Concepts and Methods, Quantitative Data and Formulae (New York: Wiley, 1982).

S Wolf, is preparing a manuscript to be called “Color Correction Matrix for Digital Still and Video Imaging Systems.”

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Figures (14)

Fig. 1.
Fig. 1.

Colposcopic images with various color and illumination.

Fig. 2.
Fig. 2.

The concept of color calibration: mapping the raw color space of different instruments into a standard color space.

Fig. 3.
Fig. 3.

Calibration Procedure

Fig. 4.
Fig. 4.

Calibration Targets used (a) Gray target, (b) Color target

Fig. 5.
Fig. 5.

Gray Flat image (a), Cross-sectional signal in CIE-Lab color Space illustrating non-uniformity in the luminosity, L, channel only (b) Cross-sectional signal in RGB space showing non-uniformity in all three channels. The CIE-lab color space is scaled to the same type as the input signal which in our case is a 16-bit signal.

Fig. 6.
Fig. 6.

High-resolution digital colposcope with stereoscopic and cross-polarized imaging capabilities.

Fig. 7.
Fig. 7.

Calibration unit (a) The major components of the calibration unit: calibration targets, filter wheel, and the tube, (calibration target cover removed) (b) Calibration unit mounted on the digital colposcope to take calibration data (including the calibration target cover).

Fig. 8.
Fig. 8.

(a) Non-uniform distribution of light, (b) Corrected distribution

Fig. 9.
Fig. 9.

Images of the color targets, (a) original image, (b) result of patch finder, (c) calibrated image

Fig. 10.
Fig. 10.

Calibration results I (Xenon lamp, Augusta data), (a), (b), and (c) are colposcopic pre-acetowhite, acetowhite, and Lugol’s iodine images, respectively, before calibration, and (d), (e), and (f) are corresponding calibrated images.

Fig. 11.
Fig. 11.

Calibration results II (Xenon lamp, Lima data), (a), (b), and (c) are colposcopic pre-acetowhite, acetowhite, and Lugol’s iodine images, respectively, before calibration, and (d), (e), and (f) are corresponding calibrated images.

Fig. 12.
Fig. 12.

Calibration results III (Halogen lamp, Honolulu data), (a) and (b) are colposcopic acetowhite images before calibration and (c) and (d) are corresponding calibrated images.

Fig. 13.
Fig. 13.

(a) Acetowhite region detection results for un-calibrated image, (b), Acetowhite region detection results on calibrated image, (c) colposcopist’s annotation. (Blue curves indicate detected regions.)

Fig. 14.
Fig. 14.

The average color values of the extracted Acetowhite regions from (a) the uncalibrated image and (b) the calibrated image in Fig. 13. pre-acetowhite, acetowhite,

Equations (3)

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

C gb ( x , y ) = C raw ( x , y ) C background ( x , y ) C grayflat ( x , y ) C graybackground ( x , y ) × S c
( A 1 B 1 C 1 A 2 B 2 C 2 A n B n C n ) = ( 1 native A 1 native B 1 native C 1 1 native A 2 native B 2 native C 2 1 native A n native B n native C n ) ( a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 a 41 a 42 a 43 )
y j = b 1 j x j 3 + b 2 j x j 2 + b 3 j x j + b 4 j

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