ANN-Based Classification of Indirect Immuno Fluorescence Images
In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
[1] J.H. Klippel, P.A. Dieppe, Rheumatology, 2nd edition, Mosby
International, 1998.
[2] A. Kavanaugh, R. Tomar, J. Reveille, et al., "Guidelines for Clinical Use
of the Antinuclear Antibody Test and Tests for Specific Autoantibodies
to Nuclear Antigens", American College of Pathologists, Archives of
Pathology and Laboratory Medicine, Vol. 124, No.1, 2000, pp. 71-81.
[3] R. Marcolongo et al., "Presentazione Linee Guida del Forum
Interdisciplinare per la Ricerca sulle Malattie Autoimmuni
(F.I.R.M.A.)", Reumatismo, Vol. 55, 2003, pp. 9-21.
[4] Service Manual AP16 IF Plus, Das s.r.l. , Palombara Sabina (RI), March
2004.
[5] PhD System, Bio-Rad Laboratories Inc., 2004, Available:
http://www.bio-rad.com.
[6] D.H. Solomon, A. Kavanaugh, et al., "Evidence-Based Guidelines for
the Use of Immunologic Tests: Antinuclear Antibody Testing", Arthritis
& Rheumatism, Vol. 47, No. 4, 2002, pp. 434-444.
[7] A.K. Jain, R.P.W. Duin, J. Mao, "Statistical Pattern Recognition:
A Review", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 22, No. 1, January 2000, pp. 4-37.
[8] M. De Santo, F. Tortorella, M. Molinara, M. Vento, "Automatic
Classification of Clustered Microcalcifications by a Multiple Expert
System", Pattern Recognition, Vol. 36, 2003, pp. 1467-1477.
[9] P. Perner, H. Perner, B. Muller, "Mining Knowledge for HEp-2 Cell
Image Classification", Journal Artificial Intelligence in Medicine,
Vol. 26, 2002, pp. 161-173.
[10] U. Sack, S. Knoechner, H. Warschkau, U. Pigla, F. Emmerich,
M. Kamprad, "Computer-Assisted Classification of HEp-2
Immunofluorescence Patterns in Autoimmune Diagnostics",
Autoimmunity Reviews, Vol. 2, 2003, pp. 298-304.
[11] J. Cohen, "A coefficient of agreement for nominal scales", Education
and Psychological Measurement, Vol. 20, 1960, pp. 37-46.
[12] J.R. Landis, G.G. Koch, "The measurement of observer agreement for
categorical data", Biometrics, Vol.33, 1997, pp. 159-174.
[13] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms,"
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1,
1979, pp. 62-66.
[14] M. Egmont-Petersen, D. de Ridder, H. Handels, "Image Processing with
Neural Networks - a review", Pattern Recognition, Vol. 35, 2002,
pp. 2279-2301.
[15] T. Fawcett, "ROC Graphs: Notes and Practical Considerations for Data
Mining Researchers", HP Laboratories, January 2003.
[16] K. Fukunaga, D.M. Hummels, "Leave-One-Out Procedures for
Nonparametric Error Estimates", IEEE Transactions on Pattern Analysis
and Machine Intelligence, Vol. 11, No. 4, April 1989, pp. 421-423.
[17] A.P. Bradley, "The Use of the Area Under the Roc Curve in the
Evaluation of Machine Learning Algorithms", Pattern Recognition,
Vol. 30, No. 7, 1997, pp. 1145-1159.
[18] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice
Hall, NJ, USA, 1998.
[1] J.H. Klippel, P.A. Dieppe, Rheumatology, 2nd edition, Mosby
International, 1998.
[2] A. Kavanaugh, R. Tomar, J. Reveille, et al., "Guidelines for Clinical Use
of the Antinuclear Antibody Test and Tests for Specific Autoantibodies
to Nuclear Antigens", American College of Pathologists, Archives of
Pathology and Laboratory Medicine, Vol. 124, No.1, 2000, pp. 71-81.
[3] R. Marcolongo et al., "Presentazione Linee Guida del Forum
Interdisciplinare per la Ricerca sulle Malattie Autoimmuni
(F.I.R.M.A.)", Reumatismo, Vol. 55, 2003, pp. 9-21.
[4] Service Manual AP16 IF Plus, Das s.r.l. , Palombara Sabina (RI), March
2004.
[5] PhD System, Bio-Rad Laboratories Inc., 2004, Available:
http://www.bio-rad.com.
[6] D.H. Solomon, A. Kavanaugh, et al., "Evidence-Based Guidelines for
the Use of Immunologic Tests: Antinuclear Antibody Testing", Arthritis
& Rheumatism, Vol. 47, No. 4, 2002, pp. 434-444.
[7] A.K. Jain, R.P.W. Duin, J. Mao, "Statistical Pattern Recognition:
A Review", IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. 22, No. 1, January 2000, pp. 4-37.
[8] M. De Santo, F. Tortorella, M. Molinara, M. Vento, "Automatic
Classification of Clustered Microcalcifications by a Multiple Expert
System", Pattern Recognition, Vol. 36, 2003, pp. 1467-1477.
[9] P. Perner, H. Perner, B. Muller, "Mining Knowledge for HEp-2 Cell
Image Classification", Journal Artificial Intelligence in Medicine,
Vol. 26, 2002, pp. 161-173.
[10] U. Sack, S. Knoechner, H. Warschkau, U. Pigla, F. Emmerich,
M. Kamprad, "Computer-Assisted Classification of HEp-2
Immunofluorescence Patterns in Autoimmune Diagnostics",
Autoimmunity Reviews, Vol. 2, 2003, pp. 298-304.
[11] J. Cohen, "A coefficient of agreement for nominal scales", Education
and Psychological Measurement, Vol. 20, 1960, pp. 37-46.
[12] J.R. Landis, G.G. Koch, "The measurement of observer agreement for
categorical data", Biometrics, Vol.33, 1997, pp. 159-174.
[13] N. Otsu, "A Threshold Selection Method from Gray-Level Histograms,"
IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1,
1979, pp. 62-66.
[14] M. Egmont-Petersen, D. de Ridder, H. Handels, "Image Processing with
Neural Networks - a review", Pattern Recognition, Vol. 35, 2002,
pp. 2279-2301.
[15] T. Fawcett, "ROC Graphs: Notes and Practical Considerations for Data
Mining Researchers", HP Laboratories, January 2003.
[16] K. Fukunaga, D.M. Hummels, "Leave-One-Out Procedures for
Nonparametric Error Estimates", IEEE Transactions on Pattern Analysis
and Machine Intelligence, Vol. 11, No. 4, April 1989, pp. 421-423.
[17] A.P. Bradley, "The Use of the Area Under the Roc Curve in the
Evaluation of Machine Learning Algorithms", Pattern Recognition,
Vol. 30, No. 7, 1997, pp. 1145-1159.
[18] S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice
Hall, NJ, USA, 1998.
@article{"International Journal of Information, Control and Computer Sciences:57704", author = "P. Soda and G.Iannello", title = "ANN-Based Classification of Indirect Immuno Fluorescence Images", abstract = "In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
", keywords = "Artificial neural networks, computer aided diagnosis, image classification, indirect immuno-fluorescence, pattern recognition.", volume = "2", number = "8", pages = "2718-6", }
