Abstract

Diagnostic cytology, which is used to screen for cervical cancer, involves characterizing cellular features such as shape, size, and texture. Automated screening of cervical smear slides is desirable but computationally challenging since each slide requires processing 2 × 109 pixels at a resolution of 0.8 μm per pixel. We demonstrate that the throughput of optical processors can be exploited in automated cervical smear-screening systems. In particular, we identify a morphological shape detector to perform the initial region of interest (ROI) detection and to demonstrate experimentally its optoelectronic implementation. The ROI detector is tested on 200 images, and its performance is characterized as a receiver operating characteristic (ROC). The area under the ROC curve is as high as 96.4% of the total area. The simulation and the experimental results are found comparable, and the discrepancy between the two results is determined to be a function of the number of bits represented in the filter plane device.

© 1998 Optical Society of America

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1996

D. H. Grohs, “Challenges in cervical cancer screening: what clinicians, patients and the general public need to know,” Acta Cytol. 40, 133–137 (1996).
[CrossRef] [PubMed]

1995

1994

1993

K. R. Castleman, K. H. Price, B. S. White, “Effects of random abnormal cell proportion on specimen classifier performance,” Cytometry 14, 1–8 (1993).
[CrossRef]

J. Garcia, T. Szoplik, C. Ferreira, “Optoelectronic morphological image processor,” Opt. Lett. 18, 1952–1954 (1993).
[CrossRef] [PubMed]

1992

1991

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Editorial Office of AQCH and IAC committee on Quantitative Morphology, “Data on automated cytology systems as submitted by their developers,” Anal. Quantitative Cytol. Histol. 13, 300–306 (1991).

1990

P. S. Oud, A. A. Hurley, K. L. Douglass, “Sample preparation in diagnostic cytology: present status and new developments,” Diagn. Clin. Test. 28, 16–19 (1990).

J. A. Neff, R. A. Athale, S. H. Lee, “Two-dimensional spatial light modulators: a tutorial,” Proc. IEEE, 78, 826–855 (1990).

1989

1988

J. M. Hereford, W. T. Rhodes, “Nonlinear optical image filtering by time-sequential threshold decomposition,” Opt. Eng. 27, 274–279 (1988).
[CrossRef]

1987

1986

J. Serra, “Introduction to mathematical morphology,” Comput. Vision. Graph. Image Process. 35, 283–305 (1986).
[CrossRef]

1985

T. R. Crimmins, W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60–69 (1985).
[CrossRef]

1984

D. Psaltis, E. G. Paek, S. S. Venkatesh, “Optical image correlation with a binary spatial light modulator,” Opt. Eng. 23, 698–704 (1984).
[CrossRef]

1981

D. Pycock, C. J. Taylor, “The Magiscan image analyser as a diagnostic aid in cytology,” Anal. Quantitative Cytol. 3, 49–54 (1981).

B. S. White, K. R. Castleman, “Estimating cell population,” Pattern Recog. Lett. 13, 365–370 (1981).
[CrossRef]

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

1980

K. R. Castleman, B. S. White, “Optimizing cervical cell classifiers,” Anal. Quantitative Cytol. Histol. 2, 117–122 (1980).

K. D. Kunze, W. R. Herrmann, R. Meyer, “The ZYPAB image-processing system for cytologic prescreening for cervical cancer,” Anal. Quantitative Cytol. 2, 252–256 (1980).

J. Vrolijk, P. L. Pearson, J. S. Ploem, “LEYTAS: a system for the processing of microscopic images,” Anal. Quantitative Cytol. 2, 41–48 (1980).

1979

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

G. Abmayr, G. Burger, H. J. Soost, “Progress report of the TUDAB project for automated cancer cell detection,” J. Histochem. Cytochem. 27, 604–612 (1979).
[CrossRef]

1976

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

J. H. Tucker, “CERVISCAN: an image analysis system for experiments in automatic cervical smear prescreening,” Comput. Biomed. Res. 9, 93–107 (1976).
[CrossRef] [PubMed]

O. A. N. Husain, J. H. Tucker, B. A. P. Roberts, “Automation in cervical cancer screening 1. Fixed cell scanning systems,” Biomed. Eng. 11, 161–166 (1976).
[PubMed]

1975

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

Abmayr, G.

G. Abmayr, G. Burger, H. J. Soost, “Progress report of the TUDAB project for automated cancer cell detection,” J. Histochem. Cytochem. 27, 604–612 (1979).
[CrossRef]

Adelh, D.

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

Antonsson, D.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Athale, R. A.

J. N. Mait, D. W. Prather, R. A. Athale, “Acousto-optic processing with electronic image feedback for morphological processing,” Appl. Opt. 31, 5688–5699 (1992).
[CrossRef] [PubMed]

J. A. Neff, R. A. Athale, S. H. Lee, “Two-dimensional spatial light modulators: a tutorial,” Proc. IEEE, 78, 826–855 (1990).

Bahr, G. F.

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Bartels, P. H.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Bengtsson, E.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Bibbo, M.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Bishop, A.

J. D. Sherris, E. S. Wells, V Davis Tsu, A. Bishop, “Cervical cancer in developing countries: a situation analysis,” in Program for Appropriate Technology in Health (PATH) (The World Bank Department of Population, Health and Nutrition, Seattle, Wash., 1993).

Bolden, S.

S. Bolden, P. A. Wingo, T. Tong, “Cancer statistics,” Ca - A cancer journal for clinicians 45, 8–30 (1995).

Botha, E. C.

Brown, W. M.

T. R. Crimmins, W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60–69 (1985).
[CrossRef]

Brugal, G.

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

Burger, G.

G. Abmayr, G. Burger, H. J. Soost, “Progress report of the TUDAB project for automated cancer cell detection,” J. Histochem. Cytochem. 27, 604–612 (1979).
[CrossRef]

Casasent, D. P.

Castleman, K. R.

K. R. Castleman, K. H. Price, B. S. White, “Effects of random abnormal cell proportion on specimen classifier performance,” Cytometry 14, 1–8 (1993).
[CrossRef]

B. S. White, K. R. Castleman, “Estimating cell population,” Pattern Recog. Lett. 13, 365–370 (1981).
[CrossRef]

K. R. Castleman, B. S. White, “Optimizing cervical cell classifiers,” Anal. Quantitative Cytol. Histol. 2, 117–122 (1980).

Chen, M.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

Crimmins, T. R.

T. R. Crimmins, W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60–69 (1985).
[CrossRef]

Danielsson, P.-E.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Davis Tsu, V

J. D. Sherris, E. S. Wells, V Davis Tsu, A. Bishop, “Cervical cancer in developing countries: a situation analysis,” in Program for Appropriate Technology in Health (PATH) (The World Bank Department of Population, Health and Nutrition, Seattle, Wash., 1993).

Douglass, K. L.

P. S. Oud, A. A. Hurley, K. L. Douglass, “Sample preparation in diagnostic cytology: present status and new developments,” Diagn. Clin. Test. 28, 16–19 (1990).

Efron, U.

U. Efron, Spatial Light Modulator Technology (Marcell Deker, New York, 1995).

Eichmann, G.

Eriksson, O.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Ferreira, C.

Follet, M. A.

Garbay, C.

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

Garcia, J.

Giroud, F.

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

Grohs, D. H.

D. H. Grohs, “Challenges in cervical cancer screening: what clinicians, patients and the general public need to know,” Acta Cytol. 40, 133–137 (1996).
[CrossRef] [PubMed]

Harris, M. J.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

Hedblom, T.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Hereford, J. M.

J. M. Hereford, W. T. Rhodes, “Nonlinear optical image filtering by time-sequential threshold decomposition,” Opt. Eng. 27, 274–279 (1988).
[CrossRef]

Herrmann, W. R.

K. D. Kunze, W. R. Herrmann, R. Meyer, “The ZYPAB image-processing system for cytologic prescreening for cervical cancer,” Anal. Quantitative Cytol. 2, 252–256 (1980).

Holmquist, J.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Hosoi, S.

Hurley, A. A.

P. S. Oud, A. A. Hurley, K. L. Douglass, “Sample preparation in diagnostic cytology: present status and new developments,” Diagn. Clin. Test. 28, 16–19 (1990).

Husain, O. A. N.

O. A. N. Husain, J. H. Tucker, B. A. P. Roberts, “Automation in cervical cancer screening 1. Fixed cell scanning systems,” Biomed. Eng. 11, 161–166 (1976).
[PubMed]

Ikeda, H.

Johnson, K. M.

Kim, D. H.

Kostrzewksi, A.

Kunze, K. D.

K. D. Kunze, W. R. Herrmann, R. Meyer, “The ZYPAB image-processing system for cytologic prescreening for cervical cancer,” Anal. Quantitative Cytol. 2, 252–256 (1980).

Lange, H.-J.

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Lee, S. H.

J. A. Neff, R. A. Athale, S. H. Lee, “Two-dimensional spatial light modulators: a tutorial,” Proc. IEEE, 78, 826–855 (1990).

Lehmacher, W.

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Li, Y.

Mait, J. N.

Martensson, A.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

McKnight, D. J.

Meyer, R.

K. D. Kunze, W. R. Herrmann, R. Meyer, “The ZYPAB image-processing system for cytologic prescreening for cervical cancer,” Anal. Quantitative Cytol. 2, 252–256 (1980).

Mukawa, A.

Narayanswamy, R.

R. Narayanswamy, “Optoelectronic region of interest detection applied to automated cervical smear screening,” Ph.D. dissertation (University of Colorado at Boulder, Boulder, Colo., 1996).

Neff, J. A.

J. A. Neff, R. A. Athale, S. H. Lee, “Two-dimensional spatial light modulators: a tutorial,” Proc. IEEE, 78, 826–855 (1990).

Nordin, B.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

O’Neill, K. S.

K. S. O’Neill, William T. Rhodes, “Morphological transformations by hybrid optical-electronic methods,” in Hybrid Image Processing, D. P. Casasent, A. G. Tescher, eds., Proc. SPIE638, 44–44 (1986).

Okamoto, K.

Olsson, T.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Oud, P. S.

P. S. Oud, A. A. Hurley, K. L. Douglass, “Sample preparation in diagnostic cytology: present status and new developments,” Diagn. Clin. Test. 28, 16–19 (1990).

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

Paek, E. G.

D. Psaltis, E. G. Paek, S. S. Venkatesh, “Optical image correlation with a binary spatial light modulator,” Opt. Eng. 23, 698–704 (1984).
[CrossRef]

Pearson, P. L.

J. Vrolijk, P. L. Pearson, J. S. Ploem, “LEYTAS: a system for the processing of microscopic images,” Anal. Quantitative Cytol. 2, 41–48 (1980).

Ploem, J. S.

J. Vrolijk, P. L. Pearson, J. S. Ploem, “LEYTAS: a system for the processing of microscopic images,” Anal. Quantitative Cytol. 2, 41–48 (1980).

Prather, D. W.

Price, K. H.

K. R. Castleman, K. H. Price, B. S. White, “Effects of random abnormal cell proportion on specimen classifier performance,” Cytometry 14, 1–8 (1993).
[CrossRef]

Psaltis, D.

D. Psaltis, E. G. Paek, S. S. Venkatesh, “Optical image correlation with a binary spatial light modulator,” Opt. Eng. 23, 698–704 (1984).
[CrossRef]

Puls, J. H.

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Pycock, D.

D. Pycock, C. J. Taylor, “The Magiscan image analyser as a diagnostic aid in cytology,” Anal. Quantitative Cytol. 3, 49–54 (1981).

Raaijmakers, M. C. T.

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

Rhodes, W. T.

J. M. Hereford, W. T. Rhodes, “Nonlinear optical image filtering by time-sequential threshold decomposition,” Opt. Eng. 27, 274–279 (1988).
[CrossRef]

Rhodes, William T.

K. S. O’Neill, William T. Rhodes, “Morphological transformations by hybrid optical-electronic methods,” in Hybrid Image Processing, D. P. Casasent, A. G. Tescher, eds., Proc. SPIE638, 44–44 (1986).

Richards, J.

Roberts, B. A. P.

O. A. N. Husain, J. H. Tucker, B. A. P. Roberts, “Automation in cervical cancer screening 1. Fixed cell scanning systems,” Biomed. Eng. 11, 161–166 (1976).
[PubMed]

Ruffing-Kullmann, B.

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Serati, R. A.

Serra, J.

J. Serra, “Introduction to mathematical morphology,” Comput. Vision. Graph. Image Process. 35, 283–305 (1986).
[CrossRef]

Sherris, J. D.

J. D. Sherris, E. S. Wells, V Davis Tsu, A. Bishop, “Cervical cancer in developing countries: a situation analysis,” in Program for Appropriate Technology in Health (PATH) (The World Bank Department of Population, Health and Nutrition, Seattle, Wash., 1993).

Soost, H. J.

G. Abmayr, G. Burger, H. J. Soost, “Progress report of the TUDAB project for automated cancer cell detection,” J. Histochem. Cytochem. 27, 604–612 (1979).
[CrossRef]

Soost, H.-J.

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Stenkvist, B.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

Szoplik, T.

Tanaka, N.

Taylor, C. J.

D. Pycock, C. J. Taylor, “The Magiscan image analyser as a diagnostic aid in cytology,” Anal. Quantitative Cytol. 3, 49–54 (1981).

Taylor, J.

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Tong, T.

S. Bolden, P. A. Wingo, T. Tong, “Cancer statistics,” Ca - A cancer journal for clinicians 45, 8–30 (1995).

Truttman, B.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

Tsunekawa, S.

Tucker, J. H.

J. H. Tucker, “CERVISCAN: an image analysis system for experiments in automatic cervical smear prescreening,” Comput. Biomed. Res. 9, 93–107 (1976).
[CrossRef] [PubMed]

O. A. N. Husain, J. H. Tucker, B. A. P. Roberts, “Automation in cervical cancer screening 1. Fixed cell scanning systems,” Biomed. Eng. 11, 161–166 (1976).
[PubMed]

Ueno, T.

Van de Walle, R. Y.

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

Venkatesh, S. S.

D. Psaltis, E. G. Paek, S. S. Venkatesh, “Optical image correlation with a binary spatial light modulator,” Opt. Eng. 23, 698–704 (1984).
[CrossRef]

Vooijs, G. P.

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

Vrolijk, J.

J. Vrolijk, P. L. Pearson, J. S. Ploem, “LEYTAS: a system for the processing of microscopic images,” Anal. Quantitative Cytol. 2, 41–48 (1980).

Watanabe, S.

Wells, E. S.

J. D. Sherris, E. S. Wells, V Davis Tsu, A. Bishop, “Cervical cancer in developing countries: a situation analysis,” in Program for Appropriate Technology in Health (PATH) (The World Bank Department of Population, Health and Nutrition, Seattle, Wash., 1993).

White, B. S.

K. R. Castleman, K. H. Price, B. S. White, “Effects of random abnormal cell proportion on specimen classifier performance,” Cytometry 14, 1–8 (1993).
[CrossRef]

B. S. White, K. R. Castleman, “Estimating cell population,” Pattern Recog. Lett. 13, 365–370 (1981).
[CrossRef]

K. R. Castleman, B. S. White, “Optimizing cervical cell classifiers,” Anal. Quantitative Cytol. Histol. 2, 117–122 (1980).

Wied, G. L.

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

Wingo, P. A.

S. Bolden, P. A. Wingo, T. Tong, “Cancer statistics,” Ca - A cancer journal for clinicians 45, 8–30 (1995).

Zahniser, D. J.

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

D. J. Zahniser, CYTYC Corporation, 85 Swanson Road, Boxborough, Mass. 01719 (personal communication, 1994).

Acta Cytol.

G. L. Wied, G. F. Bahr, M. Bibbo, J. H. Puls, J. Taylor, P. H. Bartels, “The TICAS-RTCIP real time cell identification processor,” Acta Cytol. 19, 286–288 (1975).
[PubMed]

D. H. Grohs, “Challenges in cervical cancer screening: what clinicians, patients and the general public need to know,” Acta Cytol. 40, 133–137 (1996).
[CrossRef] [PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. I. Carcinoma in situ ,” Acta Cytol. 19, 438–446 (1975).
[PubMed]

M. Bibbo, P. H. Bartels, M. Chen, M. J. Harris, B. Truttman, G. L. Wied, “The numerical composition of cellular samples from the female reproductive tract. II. Cases with invasive squamous carcinoma of uterine cervix,” Acta Cytol. 20, 249–254 (1976).
[PubMed]

H.-J. Soost, H.-J. Lange, W. Lehmacher, B. Ruffing-Kullmann, “The validation of cervical cytology: sensitivity, specificity and predictive values.” Acta Cytol. 35, 8–14 (1991).
[PubMed]

Anal. Quantitative Cytol.

J. Holmquist, D. Antonsson, E. Bengtsson, P.-E. Danielsson, O. Eriksson, T. Hedblom, A. Martensson, B. Nordin, T. Olsson, B. Stenkvist, “The Uppsala-Linkoping image processing system,” Anal. Quantitative Cytol. 3, 182–194 (1981).

D. Pycock, C. J. Taylor, “The Magiscan image analyser as a diagnostic aid in cytology,” Anal. Quantitative Cytol. 3, 49–54 (1981).

K. D. Kunze, W. R. Herrmann, R. Meyer, “The ZYPAB image-processing system for cytologic prescreening for cervical cancer,” Anal. Quantitative Cytol. 2, 252–256 (1980).

J. Vrolijk, P. L. Pearson, J. S. Ploem, “LEYTAS: a system for the processing of microscopic images,” Anal. Quantitative Cytol. 2, 41–48 (1980).

Anal. Quantitative Cytol. Histol.

Editorial Office of AQCH and IAC committee on Quantitative Morphology, “Data on automated cytology systems as submitted by their developers,” Anal. Quantitative Cytol. Histol. 13, 300–306 (1991).

K. R. Castleman, B. S. White, “Optimizing cervical cell classifiers,” Anal. Quantitative Cytol. Histol. 2, 117–122 (1980).

Appl. Opt.

Biomed. Eng.

O. A. N. Husain, J. H. Tucker, B. A. P. Roberts, “Automation in cervical cancer screening 1. Fixed cell scanning systems,” Biomed. Eng. 11, 161–166 (1976).
[PubMed]

Ca - A cancer journal for clinicians

S. Bolden, P. A. Wingo, T. Tong, “Cancer statistics,” Ca - A cancer journal for clinicians 45, 8–30 (1995).

Comput. Biomed. Res.

J. H. Tucker, “CERVISCAN: an image analysis system for experiments in automatic cervical smear prescreening,” Comput. Biomed. Res. 9, 93–107 (1976).
[CrossRef] [PubMed]

Comput. Vision. Graph. Image Process.

J. Serra, “Introduction to mathematical morphology,” Comput. Vision. Graph. Image Process. 35, 283–305 (1986).
[CrossRef]

Cytometry

K. R. Castleman, K. H. Price, B. S. White, “Effects of random abnormal cell proportion on specimen classifier performance,” Cytometry 14, 1–8 (1993).
[CrossRef]

Diagn. Clin. Test.

P. S. Oud, A. A. Hurley, K. L. Douglass, “Sample preparation in diagnostic cytology: present status and new developments,” Diagn. Clin. Test. 28, 16–19 (1990).

IEEE Trans. Aerosp. Electron. Syst.

T. R. Crimmins, W. M. Brown, “Image algebra and automatic shape recognition,” IEEE Trans. Aerosp. Electron. Syst. AES-21, 60–69 (1985).
[CrossRef]

J. Histochem. Cytochem.

G. Abmayr, G. Burger, H. J. Soost, “Progress report of the TUDAB project for automated cancer cell detection,” J. Histochem. Cytochem. 27, 604–612 (1979).
[CrossRef]

D. J. Zahniser, P. S. Oud, M. C. T. Raaijmakers, G. P. Vooijs, R. Y. Van de Walle, “BioPEPR: a system for the automatic prescreening of cervical smears,” J. Histochem. Cytochem. 27, 635–641 (1979).
[CrossRef] [PubMed]

G. Brugal, C. Garbay, F. Giroud, D. Adelh, “A double scanning microphotometer for image analysis: hardware, software and biomedical applications,” J. Histochem. Cytochem. 27, 144–153 (1979).
[CrossRef] [PubMed]

Opt. Eng.

D. Psaltis, E. G. Paek, S. S. Venkatesh, “Optical image correlation with a binary spatial light modulator,” Opt. Eng. 23, 698–704 (1984).
[CrossRef]

J. M. Hereford, W. T. Rhodes, “Nonlinear optical image filtering by time-sequential threshold decomposition,” Opt. Eng. 27, 274–279 (1988).
[CrossRef]

Opt. Lett.

Pattern Recog. Lett.

B. S. White, K. R. Castleman, “Estimating cell population,” Pattern Recog. Lett. 13, 365–370 (1981).
[CrossRef]

Proc. IEEE

J. A. Neff, R. A. Athale, S. H. Lee, “Two-dimensional spatial light modulators: a tutorial,” Proc. IEEE, 78, 826–855 (1990).

Other

U. Efron, Spatial Light Modulator Technology (Marcell Deker, New York, 1995).

Cytyc Corporation, 85 Swanson Road, Boxborough, Mass. 01719.

D. J. Zahniser, CYTYC Corporation, 85 Swanson Road, Boxborough, Mass. 01719 (personal communication, 1994).

R. Narayanswamy, “Optoelectronic region of interest detection applied to automated cervical smear screening,” Ph.D. dissertation (University of Colorado at Boulder, Boulder, Colo., 1996).

Dalsa Inc., 605 McMurray Road, Waterloo, Ontario, N2V 2E9 Canada.

K. S. O’Neill, William T. Rhodes, “Morphological transformations by hybrid optical-electronic methods,” in Hybrid Image Processing, D. P. Casasent, A. G. Tescher, eds., Proc. SPIE638, 44–44 (1986).

University of Colorado, Optoelectronic Computing Systems Center, http://www-ocs.colorado.edu .

Boulder Nonlinear Systems Inc., 1898 S. Flatiron Court, Boulder, Colo. 80301.

Melles Griot, Electro-optics Division, 4665 Nautilus Court South, Boulder, Colo. 80301-5303.

J. D. Sherris, E. S. Wells, V Davis Tsu, A. Bishop, “Cervical cancer in developing countries: a situation analysis,” in Program for Appropriate Technology in Health (PATH) (The World Bank Department of Population, Health and Nutrition, Seattle, Wash., 1993).

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Figures (14)

Fig. 1
Fig. 1

Normal and abnormal squamous epithelial cells: The cell on the left with the small nucleus and large cytoplasm is normal, whereas the cell on the right with the enlarged nucleus and reduced cytoplasm is abnormal. The small dark structures are white blood cells. This image covers a 250-μm2 area on the slide.

Fig. 2
Fig. 2

Partial overlap between feature and hit or miss kernel: Shape mismatch between the feature and the hit SE results in reduced overlap between the two. Similarly, mismatch between the feature and the miss SE results in reduced overlap between the kernel and the background. Thus one can detect a mismatched feature in the hit operation and the miss operation by setting thresholds lower than the cardinality of the hit and the miss SE, respectively.

Fig. 3
Fig. 3

ROI detection with the morphological shape detection: (a) original image and (b) result from the algorithm superimposed on the original image. The white areas indicate the detection from the thresholded-correlation version of the HMT developed in this paper.

Fig. 4
Fig. 4

Another example of the ROI detection with the thresholded-correlation HMT: (a) original image consisting of a cluster of abnormal cells and (b) result from the HMT superimposed on the original.

Fig. 5
Fig. 5

ROC for the HMT applied to images from (a) conventionally prepared Pap slides and (b) monolayer prepared Pap slides. Each diamond on the graph corresponds to the performance of the HMT for a certain parameter set of the HMT.

Fig. 6
Fig. 6

4F correlator experimental arrangement: The correlator uses an EASLM at the image plane and another EASLM at the filter plane. The correlator path length is 4 × 148 mm = 592 mm. The input illumination is linearly polarized laser light at λ = 807 nm. A CCD camera is placed at the output plane to acquire the correlated hit and miss images. The images and filters to the SLM’s are controlled by a personal computer, and images from the CCD are acquired with a frame grabber resident in the PC.

Fig. 7
Fig. 7

The 4F correlator whose schematic is shown in the Fig. 6. The SLM’s are mounted on stages that have translation, vertical, and rotation adjustments. At the top right-hand corner is the personal computer that controls data to the SLM’s. The infrared laser source is on the middle right side, and at the bottom left is the CCD camera used to acquire the output correlation image.

Fig. 8
Fig. 8

BPOF of a kernel: The process of quantizing the FT of a kernel into two phase levels distorts the kernel by enhancing the edges. This edge-enhanced kernel responds only to gray level transitions. (a) The convolution kernel that is a disk of diameter 20. (b) The corresponding binary phase representation of the kernel. (c) FT of (b), which shows the edge enhancement of the kernel, and (d), which shows a section across the edge-enhanced kernel shown in (c). Since this kernel responds mainly to edges and not to the shape interior, it is unable to perform the hit or the miss operations.

Fig. 9
Fig. 9

Intensity representation of the phase-dithered hit and miss BPOF: (a) The traditional BPOF of a circle of diameter 10 in a 256 × 256 array. (b) The traditional BPOF of an annulus with an inner diameter of 24 and outer diameter of 30. (c) The dithered version of the hit filter shown in (a). (d) The dithered version of the miss filter shown in (b). Notice that near the center (lower frequencies), the phase values of adjacent pixels are highly correlated, whereas they are random at the higher frequencies. Hence the energy from the higher frequencies does not combine constructively and thereby does not edge enhance the convolution kernel.

Fig. 10
Fig. 10

Images used to test the efficiency of filters for detection. (a) Image contains ellipses with a diameter ranging between 12 and 24 and is used to test detection as the feature shape is varied. (b) Image contains ellipses and nonelliptical features, and this image is used to study detection and false alarm. (c) Image contains partially occluded ellipses and is used to study overall shape-recognition performance in the presence of clutter. (d) This image is a key image to the image in (c) and shows the location of the ellipses to be detected.

Fig. 11
Fig. 11

HMT with the dithered filters (simulation results): (a), (b), and (c) are the unconstrained hit operation, the unconstrained miss operation, and the logical and of the hit and miss. (d), (e), and (f) are the corresponding results with the dithered BPOF’s. The dithered BPOF HMT has 100% detection but suffers slightly with a nonzero false alarm.

Fig. 12
Fig. 12

Experimentally obtained hit and miss correlations: (a) A typical 256 × 256 cervical smear image. (b) Image thresholded to binarize the image. (c) Correlation between the binarized image and the hit filter to obtain the hit correlation. (d) Correlation between the binarized image and the miss filter to obtain the miss correlation.

Fig. 13
Fig. 13

Comparing experimental and simulation HMT result: (a) Regions of interest detected from the optical correlator implementing the dithered HMT. (b) Regions of interest detected from a simulation of the dithered HMT. Barring the large nucleus on the bottom of the image, the experimental result is in agreement with the simulation result. The large nucleus on the bottom is missed owing to hardware limitations of the filter plane SLM, which is elaborated in the text.

Fig. 14
Fig. 14

ROC for the experimentally HMT results obtained for cytology images. Fifteen different thresholds are used for the hit correlation and the miss correlation, giving rise to 225 possible operating points for the HMT processor. Note that for 40% detection the false alarm rate is 6% for this dataset of 12 images.

Tables (2)

Tables Icon

Table 1 Comparing the Performance of the Unconstrained HMT, BPOF Correlation, and Dithered Filter HMT with the Test Imagesa

Tables Icon

Table 2 Five Possible Operating Points for the Experimental HMT on the Dataset of 12 Cellular Images

Equations (27)

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

F A l F c W - A h ,
F H ,   G = m | T | H | h * f m = 1 m | T | G | g * 1 - f m = 1 ,
H α ,   β = + 1 if   Re H α ,   β > 0 - 1 otherwise ,
S α ,   β = sign D × Re H α ,   β + U - 1 ,   1 α ,   β ,
A B = z R 2 | B z A ,
A B = a + b | a A ,   b B ,
F = m N 2 | f m 0 , G = m N 2 | g m 0 .
f m = 1 m F 0 m F ,
g m = 1 m G 0 m G .
T t C = 1 if   C t 0 if   C < t .
f * g m = n S   f n · g m - n ,
f g m = n S   f n · g m + n .
F G = m N 2 | T | G | g f m = 1 .
  n S   g n f n + m | G |   T | G | g f m = 1 .
n S   g n f n + m | G | .
  g n = f n + m = 1 ,   n G   whenever   n G ,   we   have   n + m F   G m F   m F G .
F G = m :   F ˆ G - m ϕ = m N 2 | T 1 f * g m = 1 .
F G = m N 2 | F ˆ G - m ϕ .
F G = m N 2 | T 1 { f * g m = 1 .
So   n F ˆ G - m - n F   &   n = b - m ,   for   b G - n F   &   n + m = b ,   for   b G .
n F   &   m - n = b G   f n 0   &   g m - n 0   n S   f n g m - n 1   T 1 f * g m = 1 .
n S   f n g m - n 1     an   n S ,   such   that   f n 0   &   g m - n 0   n F   &   m - n G   n + m - n F G   m F G .
f g m = n S   f m + n · g n ,
F G = m N 2 | T f g m = 1 .
f * g m = n S   f m - n · g n
F H ,   G = m | T | H | h f m = 1 m | T | G | g 1 - f m = 1 ,
F H ,   G = m | T 1 1 - f * h ˆ m = 1 m | T 1 f * g ˆ m = 1 c .

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