Abstract
Purpose of this work is the development of an automatic system which can be useful for radiologists in the investigation of breast and lung cancer. A breast neoplasia is often marked by the presence of microcalcifications and massive lesions in the mammogram. The first are a very small object in a noise background and the second are large object with particular shape. The need for tools able to recognize such lesions at an early stage is therefore apparent. In this article is shown an application of artificial neural network on the imaging analysis in mammography. The results obtained in terms of sensitivity and specificity when it has been tested alone and then used as second reader will be presented. We present also an overview about the methods developed for pulmonary nodule detection in CT images and the preliminary results obtained with a pre-processing filter will be also presented.
on behalf of the MAGIC-5 Collaboration
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Armato S.G., Li F., Giger M.L., MacMahon H, Sone S. Doi K. (2002): ‘Lung Cancer: Performance of Automated Lung Nodule Detection Applied to Cancers Missed in a CT Screening Program’, Radiology 225 pp. 685–692.
Bottigli U., Delogu P., Fantacci M.E., Fauci F., Golosio B., Lauria A., Palmiero R., Raso G., Stumbo S., Tangaro S. (2002): ‘Search of microcalcification cluster with the CALMA CAD station’, The International Society for Optical Engineering (SPIE) 4684 pp. 1301–1310.
Feig S.A., Yaffe M. (1995): ‘Digital mammography, computer aided diagnosis and telemammography’, Radiol. Clin. N. Am. 33, pp. 1205–1230.
Gurcan M.N., Sahiner B., Petrick N., Chan H., Kazerooni E.A., Cascade P.N., Hadjiiski L. (2002): ‘Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computer-aided diagnosis system’, Med. Phys. 29(11), pp. 2552–2558.
Itoh S., Ikeda M., Arahata S., Kodaira T., Isomura T., Kato T., Yamakawa K., Maruyama K., Ishigaki T. (2000): ‘Lung Cancer Screening: Minimum Tube Current Required for Helical CT’, Radiology 215 pp. 175–183.
Karssmejer N. (1999): ‘Reading screening mammograms with the help of neural networks’, Nederlands Tijdschriff geneeskd, 143/45, pp. 2232–2236.
Lauria A., Fantacci M.E., Bottigli U., Delogu P., Fauci F., Golosio B., Indovina P.L., Masala G.L., Palmiero R., Raso G., Stumbo S., Tangaro S. (2003): ‘Diagnostic performance of radiologists with and without different CAD systems for mammography’, The International Society for Optical Engineering (SPIE) 5034 pp. 51–56.
Li Q., Sone S., Doi K. (2003): ‘Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans’, Med. Phys. 30(8) pp. 2040–2051.
Viborny C.J., Giger M.L., Nishikawa R.M. (2000): ‘Computer aided detection and diagnosis of breast cancer’, Radiol. Clin. N. Am. 38(4), pp. 725–740.
Zell A. et al., SNNS Stuttgart Neural Network Simulator — v4.1, University of Stuttgart, report 1995
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer
About this paper
Cite this paper
Masala, G.L. (2005). A Computer Aided Analysis on Digital Images. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Biological and Artificial Intelligence Environments. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3432-6_41
Download citation
DOI: https://doi.org/10.1007/1-4020-3432-6_41
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3431-2
Online ISBN: 978-1-4020-3432-9
eBook Packages: Computer ScienceComputer Science (R0)