Abstract
It has been established that mammogram plays vital role in early detection of breast diseases. We also know that computational empowerment of mammogram facilitates relevant and significant information. Several research groups are exploring various aspects of mammograms in terms of feature selection to develop an effective automatic classification system.
Mammographic attributes including textural features, statistical features as well as structural features are used effectively to develop automatic classification systems. Several clinical trials explained that attributes of patient’s clinical profile also plays an important role in determination of class of a breast tumor. However, usage of patients clinical attributes for automatic classification and results thereof are not reported in literature. None of the existing standard mammogram datasets provide such additional information about patients history attributes.
Our focus is to validate observations revealed by clinical trials using automatic classification techniques. We have developed a dataset of mammogram images along with significant attributes of patients clinical profile. In this paper, we discuss our experiments with standard mammogram datasets as well as with our extended, informative live data set. Appropriate features are extracted from mammograms to develop Support Vector Machine (SVM) classifier. The results obtained using mere mammographic features are compared with the results obtained using extended feature set which includes clinical attributes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Raghavan, G.: Mammogram Analysis: Tumor Classification. EE 381K - Multidimensional Digital Signal Processing. Springer (2005)
Mohan Kumar, S., et al.: Statistical Features Based Classification of Microcalcification in Digital Mammogram Using Stochastic Neighbors Embedding. International Journal of Advanced Information Science and Technology (IJAIST)Â 7(7) (November 2012) ISSN: 2319:2682
Chang, R.F., et al.: Support Vector Machines for Diagnosis of Breast Tumors on US Images1. Academic Radiology 10(2) (February 2003)
Telagarapu, P., et al.: Extraction to Classify the Benign and Malignant Tumors from Breast Ultrasound Images. International Journal of Engineering and Technology (IJET) 6(1) (February-March 2014) ISSN : 0975-4024
Shi, X., Cheng, H.D., et al.: Mass Detection and Classificatio. In: Breast Ultrasound Images Using Fuzzy SVM, Utah State University 401B. Old Mail Hill, Logan UT 84322, USA
Karmilasari, Widodo, S., Lussiana, E.T.P., Hermita, M., Mawadah,L.: Classification of Mammogram Images using Support Vector Machine, saki.siit.tu.ac.th/acis2013/uploads_final/25.../ACIS%202013-0025.pdf
Zaiane, O.R., Antonie, M.L., Coma, A.: Mammography Classification By an Association Rule based Classifier. In: MDM/KDD 2002: International Workshop on multimedia Data Mining (with ACM SIGKDD) (2002)
Hejazi, M.R., Ho, Y.-S.: Automated Detection of Tumors in Mammograms Using Two Segments for Classification. In: Ho, Y.-S., Kim, H.-J. (eds.) PCM 2005. LNCS, vol. 3767, pp. 910–921. Springer, Heidelberg (2005)
Tzikopoulos, S.D., Mavroforakis, M.E., Georgiou, H.V., Dimitropoulos, N., Theodoridis, S.: A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Computer Methods and Programs in Biomedicine 102(1), 47–63 (2011)
Xu, S., Liu, H., Song, E.: Marker-controlled watershed for lesion segmentation in mammograms. Journal of Digital Imaging 24, 754–763 (2011), http://dx.doi.org/10.1007/s10278-011-9365-2 , doi:10.1007/s10278-011-9365-2
Friedenreich, C.M., et al.: Risk factors for benign proliferative breast disease. International Journal of Epidemiology 29, 637–644 (2000)
Yang, W.T., Tse, G.M.K.: Sonographic, Mammographic, and Histopathologic Correlation of Symptomatic Ductal Carcinoma In Situ. AJRÂ 182 (January 2004)
Thongkam, J., et al.: Toward breast cancer survivability prediction models through improving training space. Elsevier Ltd. (2009), doi:10.1016/j.eswa.2009.04.067
Rosenberg, R.D.: Performance Benchmarks for Screening Mammography1. Radiology 241(1) (October 2006)
Tintus-Ernstoff, L., et al.: Breast cancer risk factors in relation to breast density (United Sates). Springer Science + Business media B.V. (2006), doi:10.1007/s10552-006-0071-1
Artmann, L.C.H., et al.: Efficacy of Bilateral Prophylactic Mastectomy in Women With A Family History of Breast Cancer. The New England Journal of Medicine 340 (January 14, 1999)
Robsahm, T.E., et al.: Breast cancer incidence in food- vs. non-food-producing areas in Norway: possible beneficial effects of World War II. British Journal of Cancer 86, 362–366 (2002), doi:10.1038/sj/bjc/6600084
Must, A., et al.: Recall of Early Menstrual History and Menarcheal Body Size: After 30 Years,How Well Do Women Remember? American Journal of Epidemiology 155(7)
Roberts, M., et al.: Risk of breast cancer in women with history of benign disease of the breast. British Medical Journal 288 (January 28, 1984)
Bhale, A., Joshi, M., et al.: Development of a Standard Multimodal Mammographic Dataset. In: Proceedings of National Conference on Advances in Computing (NCAC 2013), March 5-6, North Maharashtra University, Jalgaon (2013) ISBN: 978-81-910591-7-5
Mohanty, A.K., et al.: An improved data mining technique for classification and detection of breast cancer from mammograms. Springer- Verlag London Limited (2012), doi:10.1007/s00521-012-834-4
Nick Bryan, R.: Introduction to the Science of Medical Imaging. Cambridge University Press, doi: http://dx.doi.org/10.1017/CBO9780511994685 ISBN: 9780511994685
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bhale, A., Joshi, M., Patil, Y. (2015). Role of Clinical Attributes in Automatic Classification of Mammograms. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-13728-5_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
eBook Packages: EngineeringEngineering (R0)