Computer Aided Detection on Mammography

Authors: Giovanni Luca Masala

Abstract:

A typical definition of the Computer Aided Diagnosis (CAD), found in literature, can be: A diagnosis made by a radiologist using the output of a computerized scheme for automated image analysis as a diagnostic aid. Often it is possible to find the expression Computer Aided Detection (CAD or CADe): this definition emphasizes the intent of CAD to support rather than substitute the human observer in the analysis of radiographic images. In this article we will illustrate the application of CAD systems and the aim of these definitions. Commercially available CAD systems use computerized algorithms for identifying suspicious regions of interest. In this paper are described the general CAD systems as an expert system constituted of the following components: segmentation / detection, feature extraction, and classification / decision making. As example, in this work is shown the realization of a Computer- Aided Detection system that is able to assist the radiologist in identifying types of mammary tumor lesions. Furthermore this prototype of station uses a GRID configuration to work on a large distributed database of digitized mammographic images.

Keywords: Computer Aided Detection, Computer Aided Diagnosis, mammography, GRID.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074787

References:

[1] U. Bick and K. Doi, Computer Aided Diagnosis Tutorial, CARS 2000 "Tutorial on Computer Aided-Diagnosis". Hyatt Regency: San Francisco, USA, 2000.
[2] Meyers, Nice, Becker, Nettleton, Sweeney, Meckstroth, "Automated computer analysis of radiographic images". Radiology 83: 1029-1033, 1964.
[3] F. Winsberg, M. Elkin, J. Macy, V. Bordaz, W. Weymouth, "Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis", Radiology 89: 211-215, 1967.
[4] M. Tasto, ÔÇ×Automatische Mammographie-Auswertung: Erkennung von Mikroverkalkungen", Biomedizinische Technik 20: 273-274, 1975.
[5] K. Doi, H. MacMahon, S. Katsuragawa, RM. Nishikawa, Y. Jiang, "Computer-aided diagnisis in radiology: potential and pitfalls", Eur J Radiol 31: 97-109, 1999.
[6] ML. Giger, Z. Huo, MA. Kupinsky, CJ Vyborny, "Computer -aided diagnosis in mammography. In : M. Sonka, JM Fitzpatrick, "Handbook of medical imaging", volume 2. Medical image processing and analysis. SPIE, Bellingham:915-1004, 2000.
[7] N. Karssemeijer, JHCL Hendriks, "Computer-assisted reading of mammograms" Eur Radiol 7: 743-748, 1997.
[8] CJ. Vyborny, ML. Giger, "Computer vision and artificial intelligence in mammography". AJR 162: 699-708, 1994.
[9] 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 Masses Lesion Classification with Neural Network, IEEE Transactions on Nuclear Science (TNS) Vol. 53, No. 4 ,August 2006.
[10] Bellotti R., De Carlo F., Gargano G., Maggipinto G., Tangaro S., Castellano M., Massafra R., Cascio D., Fauci F., Magro R., Raso G., Lauria A., Forni G., Bagnasco S., Cerello P.,Cheran S.C., Lopez Torres E., Bottigli U., Masala G.L., Oliva P., Retico A., Fantacci M.E., Cataldo R., De Mitri I., De Nunzio G., "A completely automated CAD system for mass detection in a large mammographic database", Medical Physics, Vol. 33, No. 8, pp. 3066-3075, Aug 2006.
[11] 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",Nuovo Cimento C, Vol 028, Issue 06, pp 905-912 , 2005.
[12] S.Bagnasco, U. Bottigli, P. Cerello, S. Cheran, P. Delogu, M.E. Fantacci, F. Fauci, G. Forni, A. Lauria, E. Lopez Torres, R. Magro, G.L. Masala, P. Oliva R. Palmiero, L. Ramello, G. Raso, A. Retico, M. Sitta, S. Stumbo, S. Tangaro, E. Zanon ": GPCALMA: a GRID based tool for mammographic screening, Methods of Information in Medicine 44(2) pp. 244-48, 2005.
[13] U. Bottigli, R.Chiarucci, B. Golosio, G.L. Masala, P. Oliva, S.Stumbo, D.Cascio, F. Fauci, M. Glorioso, M. Jacomi, R. Magro and G. Raso , "Superior Performances of the Neural Network on the Masses Lesions Classification through Morphological Lesion Differences" on IJBS International Journal of Biomedical Sciences, Volume 1 Number 1, pp.56-63, 2006.
[14] A. Lauria, R. Massafra, S. Tangaro, R. Bellotti, M. Fantacci, P. Delogu, E. Lopez Torres, P. Cerello, F. Fauci, R. Magro, U. Bottigli, "GPCALMA: an Italian mammographic database of digitized images for research" proceedings of IWDM, Manchester 18-21 Giugno 2006, Lecture Notes in Computer Science 4046, Springer, 2006.
[15] M. Ulissey, J. Roehrig, "Mammography Computer Aided Detection", eMedicine.com, 2005.
[16] R2 technologies, http://www.r2tech.com.
[17] G. L. Masala, "Pattern Recognition Techniques Applied To Biomedical Patterns" on IJBS International Journal of Biomedical Sciences, Volume 1 Number 1, pp. 47-56, 2006
[18] O. Duda, P. E. Hart, D. G. Stark, "Pattern Classification", second edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.
[19] Hanley JA, McNeil B, The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology: 143; 29-36, 1982.
[20] 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.
[21] R.A. Smith, "Epidemiology of breast cancer", in "A categorical course in physics. Imaging considerations and medical physics responsibilities", Madison, Wisconsin, , Medical Physics Publishing, 1991.
[22] Lancet, 355, pp. 1822-1823, 2000.
[23] Blanks, British Medical Journal 321, 655-659, 2000.
[24] A.G. Haus and M.Yaffe (editors), "A categorical course in physics. Technical aspects in breast imaging". Radiological Society of North America, Presented at the 79th Scientific Assembly and Annual Meeting of RSNA, 1993.
[25] S.A. Feig, M.Yaffe, "Digital mammography, computer aided diagnosis and tele-mammography", Radiol. Clin. N. Am. 33, 1205-1230, 1995.
[26] S. Keddache, A. Thilander-Klang, B. Lanhede, "Storage phosphor and film screen mammography: performance with different mammographic techniques" Eur. Radiol. 9, 591-597, 1999.
[27] R.A. Scmidt, R.M.Nishikawa, "Clinical use of digital mammography: the presents and the perspects", Digit. Imaging 8/1 suppl. 74-79, 1995.
[28] N. Karssemejer, "A stochastic method for automated detection of microcalcifications in digital mammograms" in Information processing in medical imaging, Springer-Verlag New York, 227-238, 1991.
[29] N. Karssmejer, "Reading screening mammograms with the help of neural networks", Nederlands Tijdschriff geneeskd, 143/45, 2232-2236, 1999.
[30] C.J. Viborny, M.L. Giger, "Computer vision and artificial intelligence in mammography", AJR 162, 699-708, 1994.
[31] S.A. Feig and M.Yaffe, Radiologic Clinics of North America, Vol.33 n.6, 1205, 1995.
[32] R.E. Bird, "Professional quality assurance for mammographic programs", Radiology 177, 587-592, 1990.
[33] E.L. Thurfjell, K.A. Lernevall, A.A.S. Taube, "Benefit of independent double reading in a population based mammography screening program" Radiology 191, 241-244, 1994.
[34] P. Delogu, M. E. Fantacci, A. Preite Martinez, A. Retico, A. Stefanini and A. Tata: A Scalable System for Microcalcification Cluster Automated Detection in a Distributed Mammographic Database; Nuclear Science Symposium Conference Record, 2005 IEEE Volume 3, October 23 - 29, pp.1530 - 1534, 2005.