Massive Lesions Classification using Features based on Morphological Lesion Differences
Purpose of this work is the development of an
automatic classification system which could be useful for radiologists
in the investigation of breast cancer. The software has been designed
in the framework of the MAGIC-5 collaboration.
In the automatic classification system the suspicious regions with
high probability to include a lesion are extracted from the image as
regions of interest (ROIs). Each ROI is characterized by some
features based on morphological lesion differences.
Some classifiers as a Feed Forward Neural Network, a K-Nearest
Neighbours and a Support Vector Machine are used to distinguish the
pathological records from the healthy ones.
The results obtained in terms of sensitivity (percentage of
pathological ROIs correctly classified) and specificity (percentage of
non-pathological ROIs correctly classified) will be presented through
the Receive Operating Characteristic curve (ROC). In particular the
best performances are 88% ± 1 of area under ROC curve obtained
with the Feed Forward Neural Network.
[1] Smith R.A., "Epidemiology of breast cancer", in "A categorical course in
physics. Imaging considerations and medical physics responsibilities",
Madison, Winsconsin, Medical Physics Publishing, 1991.
[2] Peto R., Boreham J., Clarke m., Davies c., Deral V, correspondence "UK
and USA Breast cancer deaths down 25% in year 2000 at ages 20-69
years", LANCET 2000, 355, (9217) pp. 1822-1823, 2000.
[3] Bird R., Wallace T., Yankaskas B., "Analysis of cancer missed at
screening mammography", Radiology 1992: 184; pp.613-617, 1992.
[4] Bottigli U, Delogu P, Fantacci ME, Fauci F, Golosio B, Lauria A,
Palmiero R, Raso G, Stumbo S, Tangaro S Search of Microcalcification
clusters with the CALMA CAD station. The International Society for
Optical Engineering (SPIE) 4684: 1301-1310, 2002
[5] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo,
G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R.
Magro, G. L. Masala, P.Oliva, M. Quarta, G. Raso, A. Retico,
S.Tangaro, Mammogram Segmentation by Contour Searching and
Massive Lesion Classification with Neural Network, Proc. IEEE
Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2-
373/1-5, 2004.
[6] O. Duda, P. E. Hart, D. G. Stark, "Pattern Classification", second
edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.
[7] S. J. Russel, P.Norvig, "Artificial Intelligence. A modern approach",
UTET, 1998.
[8] S. Haykin "Neural Networks - A comprehensive foundation", second
edition, Prentice Hall, 1999.
[9] V. N. Vapnik. "Statistical Learning Theory. Wiley", New York , 1998.
[10] M. Pontil, A. Verri "Properties of Support Vector Machines", Neural
Computation, Vol. 10, pp 955-974, 1998.
[11] N. Cristianini, J. Shave-Taylor. "An Introduction to Support Vector
Machine"(and other kernel-based learning methods). Cambridge
University Press 2000.
[12] SVM_light software is available in the following location :
ftp://ftp-ai.cs.unidortmund.de/pub/Users/thorsten/svm_light/current/
svm_light.tar.gz
[13] T. Joachims, Text Categorization with Support Vector Machines:
Learning with Many Relevant Features, Proc. 10th European Conf.
Machine Learning (ECML), Springer-Verlag, 1998.
[14] T. Mitchell "Machine Learning" , McGraw-Hill 1997.
[15] Timp S., Karssemeijer N., A new 2D segmentation method based on
dynamic programming applied to computer aided detection in
mammography, Medical Physics: 31; 958-971, 2004.
[16] Baydush A.H., Catarious D.M., Abbey C.K., Floyd C.E., Computer
aided detection of masses in mammography using subregion Hotelling
observers, Medical Physics: 30; 1781-1787, 2003.
[17] Tourassi G.D., Vargas-Voracek R., Catarious D.M. Jr, Floyd C.E. Jr,
Computer-assisted detection of mammographic masses: A template
matching scheme based on mutual information, Medical Physics: 30 (8);
2123-2130, 2003.
[18] Antonie M.L., Zaiane O.R., Coman A., Application of data mining
techniques for medical image classification, Proc. of II Int. Work. On
Multimedia Data Mining, USA, 2001.
[19] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in
mammography, AJR: 162; 699-708, 1994.
[20] Lai S., Li X., Bischof W., On techniques for detecting circumscribed
masses in mammograms", IEEE Transaction on Medical Imaging: 8(4);
377-386, 1989.
[21] Hanley JA, McNeil B, The meaning and use of the area under a receiver
operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.
[22] Hanley JA, McNeil B, A method of comparing the areas under receiver
operating characteristic curves derived from the same cases, Radiology:
148; 839-843, 1983.
[23] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F.
Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De
Mitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C.
Cheran, E.Lopez Torres "Dissimilarity Application for Medical Imaging
Classification" on proceedings of The 9th World Multi-Conference on
Systemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13
July 2005, vol III pag 258-262, 2005.
[24] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. C
Cheran, E. L. Torres, "Classifiers trained on dissimilarity representation
of medical pattern: a comparative study" on Nuovo Cimento C, Vol
028, Issue 06, pp 905-912 , 2005.
[1] Smith R.A., "Epidemiology of breast cancer", in "A categorical course in
physics. Imaging considerations and medical physics responsibilities",
Madison, Winsconsin, Medical Physics Publishing, 1991.
[2] Peto R., Boreham J., Clarke m., Davies c., Deral V, correspondence "UK
and USA Breast cancer deaths down 25% in year 2000 at ages 20-69
years", LANCET 2000, 355, (9217) pp. 1822-1823, 2000.
[3] Bird R., Wallace T., Yankaskas B., "Analysis of cancer missed at
screening mammography", Radiology 1992: 184; pp.613-617, 1992.
[4] Bottigli U, Delogu P, Fantacci ME, Fauci F, Golosio B, Lauria A,
Palmiero R, Raso G, Stumbo S, Tangaro S Search of Microcalcification
clusters with the CALMA CAD station. The International Society for
Optical Engineering (SPIE) 4684: 1301-1310, 2002
[5] F. Fauci, S. Bagnasco, R. Bellotti, D. Cascio, S. C. Cheran, F. De Carlo,
G. De Nunzio, M. E. Fantacci, G. Forni, A. Lauria, E.Lopez Torres, R.
Magro, G. L. Masala, P.Oliva, M. Quarta, G. Raso, A. Retico,
S.Tangaro, Mammogram Segmentation by Contour Searching and
Massive Lesion Classification with Neural Network, Proc. IEEE
Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2-
373/1-5, 2004.
[6] O. Duda, P. E. Hart, D. G. Stark, "Pattern Classification", second
edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.
[7] S. J. Russel, P.Norvig, "Artificial Intelligence. A modern approach",
UTET, 1998.
[8] S. Haykin "Neural Networks - A comprehensive foundation", second
edition, Prentice Hall, 1999.
[9] V. N. Vapnik. "Statistical Learning Theory. Wiley", New York , 1998.
[10] M. Pontil, A. Verri "Properties of Support Vector Machines", Neural
Computation, Vol. 10, pp 955-974, 1998.
[11] N. Cristianini, J. Shave-Taylor. "An Introduction to Support Vector
Machine"(and other kernel-based learning methods). Cambridge
University Press 2000.
[12] SVM_light software is available in the following location :
ftp://ftp-ai.cs.unidortmund.de/pub/Users/thorsten/svm_light/current/
svm_light.tar.gz
[13] T. Joachims, Text Categorization with Support Vector Machines:
Learning with Many Relevant Features, Proc. 10th European Conf.
Machine Learning (ECML), Springer-Verlag, 1998.
[14] T. Mitchell "Machine Learning" , McGraw-Hill 1997.
[15] Timp S., Karssemeijer N., A new 2D segmentation method based on
dynamic programming applied to computer aided detection in
mammography, Medical Physics: 31; 958-971, 2004.
[16] Baydush A.H., Catarious D.M., Abbey C.K., Floyd C.E., Computer
aided detection of masses in mammography using subregion Hotelling
observers, Medical Physics: 30; 1781-1787, 2003.
[17] Tourassi G.D., Vargas-Voracek R., Catarious D.M. Jr, Floyd C.E. Jr,
Computer-assisted detection of mammographic masses: A template
matching scheme based on mutual information, Medical Physics: 30 (8);
2123-2130, 2003.
[18] Antonie M.L., Zaiane O.R., Coman A., Application of data mining
techniques for medical image classification, Proc. of II Int. Work. On
Multimedia Data Mining, USA, 2001.
[19] Vyborny CJ., Giger ML., Computer vision and artificial intelligence in
mammography, AJR: 162; 699-708, 1994.
[20] Lai S., Li X., Bischof W., On techniques for detecting circumscribed
masses in mammograms", IEEE Transaction on Medical Imaging: 8(4);
377-386, 1989.
[21] Hanley JA, McNeil B, The meaning and use of the area under a receiver
operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.
[22] Hanley JA, McNeil B, A method of comparing the areas under receiver
operating characteristic curves derived from the same cases, Radiology:
148; 839-843, 1983.
[23] U. Bottigli, B. Golosio, G. L. Masala, P. Oliva, S. Stumbo, D. Cascio, F.
Fauci, R. Magro, G. Raso, R. Bellotti, F. De Carlo, S.Tangaro, I. De
Mitri, G. De Nunzio, M. Quarta, A. Preite Martinez, P. Cerello, S. C.
Cheran, E.Lopez Torres "Dissimilarity Application for Medical Imaging
Classification" on proceedings of The 9th World Multi-Conference on
Systemics, Cybernetics and Informatics WMSCI 2005, Orlando 10-13
July 2005, vol III pag 258-262, 2005.
[24] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. C
Cheran, E. L. Torres, "Classifiers trained on dissimilarity representation
of medical pattern: a comparative study" on Nuovo Cimento C, Vol
028, Issue 06, pp 905-912 , 2005.
@article{"International Journal of Medical, Medicine and Health Sciences:63758", author = "U. Bottigli and D.Cascio and F. Fauci and B. Golosio and R. Magro and G.L. Masala and P. Oliva and G. Raso and S.Stumbo", title = "Massive Lesions Classification using Features based on Morphological Lesion Differences", abstract = "Purpose of this work is the development of an
automatic classification system which could be useful for radiologists
in the investigation of breast cancer. The software has been designed
in the framework of the MAGIC-5 collaboration.
In the automatic classification system the suspicious regions with
high probability to include a lesion are extracted from the image as
regions of interest (ROIs). Each ROI is characterized by some
features based on morphological lesion differences.
Some classifiers as a Feed Forward Neural Network, a K-Nearest
Neighbours and a Support Vector Machine are used to distinguish the
pathological records from the healthy ones.
The results obtained in terms of sensitivity (percentage of
pathological ROIs correctly classified) and specificity (percentage of
non-pathological ROIs correctly classified) will be presented through
the Receive Operating Characteristic curve (ROC). In particular the
best performances are 88% ± 1 of area under ROC curve obtained
with the Feed Forward Neural Network.", keywords = "Neural Networks, K-Nearest Neighbours, SupportVector Machine, Computer Aided Diagnosis.", volume = "1", number = "12", pages = "657-5", }
