Automatic breast - line and pectoral muscle segmentation

• Autorzy: Czaplicka K. , Włodarczyk J.
• Języki publikacji: EN

Abstrakty

EN

Pre-processing of mammograms is a crucial step in computer-aided analysis systems. The aim of segmentation is to extract a breast region by estimation of a breast skin-line and a pectoral muscle as well as removing radiographic artifacts and the background of the mammogram. Knowledge of the breast contour also allows further analysis of breast abnormalities such as bilateral asymmetry. In this paper we propose a fully automatic algorithm for segmentation of a breast region, based on two types of global image thresholding: the multi-level Otsu and minimizing the measure of fuzziness as well as the gradient estimation and linear regression. The results of our experiments showed that our method can be used to nd a breast line and a pectoral muscle accurately.

Słowa kluczowe

EN

pectoral muscle; breast boundary; mammogram; segmentation

• Wydawca: Wydawnictwo Uniwersytetu Jagiellońskiego
• Czasopismo: Schedae Informaticae
• Rocznik: 2011
• Tom: Vol. 20
• Strony: 195--209
• Opis fizyczny: Bibliogr. 36 poz., rys.

Twórcy

autor

Czaplicka K.

autor

Włodarczyk J.

• IPPT PAN - UJ joint Ph.D. studies of Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Reymonta 4, 30-059 Kraków, Poland,

Bibliografia

• [1] National Cancer Registry. Available via http://epid.coi.waw.pl/krn.
• [2] Dziukowa J., Weso lowska E.; Mammograa w diagnostyce raka sutka, MediPage, Warszawa 2006.
• [3] Wirth M.A., Stapinski A.; Segmentation of the breast region in mammograms using active contours, 1st Canadian Conference on Computer and Robot Vision, 2004, pp. 385-92.
• [4] Subashini T.S., Ramalingam V., Palanivel S.; Pectoral Muscle removal and Detection of masses in Digital Mammogram using CCL, International Journal of Computer Applications, 1(6), 2010, pp. 66-70.
• [5] Raba D., Oliver A., Marti J., Peracaula M. et al.; Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms, Pattern Recognition and Image Analysis, Springer, 3523, 2005, pp. 471-478.
• [6] Mendez A., Tahoces P., Lado M.J. et al.; Automatic detection of breast border and nipple in digital mammograms, Computer methods and programs in biomedicine, 49(3), 1996, pp. 253-262.
• [7] Zhou C., Chan H.P., Petrick N. et al.; Computerized image analysis: estimation of breast density on mammograms, Medical Physics, 28(6), 2001, pp. 1056-1069.
• [8] Kwok S.M., Chandrasekhar R., Attikiouzel Y.; Automatic pectoral muscle segmentation on mammograms by straight line estimation and cli detection, Proceedings of the Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001, pp. 67-72.
• [9] Karssemeijer N., Brake G.; Combining single view features and asymmetry for detection of mass lesions, 4th International Workshop on Digital Mammography, 1998.
• [10] Marti R., Oliver A., Raba D. et al.; Breast Skin-Line Segmentation Using Contour Growing, Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, 4478/2007, 2007, pp. 564-571.
• [11] Wirth M.A., Stapinski A.; Segmentation of the breast region in mammograms using active contours, Visual Communications and Image Processing, 5150, 1995, pp. 1995-2006.
• [12] Liang J., McInerney T., Terzopoulos D.; United Snakes, Medical Image Analysis, 10, 2006, pp. 215-233.
• [13] Saha P., Udupa J.; Breast tissue density quantication via digitized mammograms, IEEE Transactions on Medical Imaging, 20, 2001, pp. 792-803.
• [14] Tromans C., Brady J., Warren R.; A high accuracy technique for breast air boundary segmentation and the resulting improvement from its use in breast density estimation, International Workshop on Digital Mammography, 2004, pp. 17-18.
• [15] Wang L., Zhu M., Deng L. et al.; Automatic pectoral muscle boundary detection in mammograms based on Markov chain and active contour model, Journal of Zhejiang University-SCIENCE C (Computers & Electronics), 11(2), 2010, pp. 111-118.
• [16] Wirth M., Nikitenko D., Lyon J.; Segmentation of the Breast Region in Mammograms using a Rule-Based Fuzzy Reasoning Algorithm, ICGST International Journal on Graphics, Vision and Image Processing, 5(2), 2005, pp. 45-54.
• [17] Pilot European Image Processing Archive's database of mammograms. Available via http://peipa.essex.ac.uk/info/mias.html.
• [18] Liao P.S., Chen T.S., Chung P.C.; A fast algorithm for multilevel thresholding, Journal of Information Science and Engineering, 17(5), 2001, pp. 713-727.
• [19] Huang L.-K., Wang M.-J. J.; Image Thresholding by Minimizing the Measures of Fuzziness, Pattern Recognition, 28(1), 1995, pp. 41-51.
• [20] Kinoshita S.K., Azevedo-Marques P.M., Pereira R.R. et al.; Radon-Domain Detection of the Nipple and the Pectoral Muscle in Mammograms, Journal of Digital Imaging, 21(1), 2007, pp. 37-49.
• [21] Hong B., Brady M.; A Topographic Representation for Mammogram Segmentation, Medical Image Computing and Computer-Assisted Intervention, 2, 2003, pp. 730-737.
• [22] Carvalho I.M., Luz L.M.S., Alvarenga A.V. et al.; An Automatic Method for Delineating the Pectoral Muscle in Mammograms, IFMBE Proceedings, 18(2), 2008, pp. 271-275.
• [23] Xu W.D., Li L.H., Liu W.; A Novel Pectoral Muscle Segmentation Algorithm Based on Polyline Fitting and Elastic Thread Approaching, The 1st International Conference on Bioinformatics and Biomedical Engineering, 2007, pp. 837-840.
• [24] Hong B.W., Sohn B.S.; Segmentation of Regions of Interest in Mammograms in a Topographic Approach, IEEE Transactions on Information Technology in Biomedicine, 14(1), 2010, pp. 129-139.
• [25] Ojala T., Liang J.; Interactive segmentation of the breast region from digitized mammograms with united snakes, Technical Report 315, Turku Centre for Computer Science, 1999.
• [26] Karssemeijer N.; Automated classication of parenchymal patterns in mammograms, Physics in Medicine and Biology, 43(2), 1998.
• [27] Lau T., Bischof W.F.; Automated Detection of Breat Tumors Using the Assymetry Approach, Computers and Biomedical Research, 24, 1991, pp. 273-295.
• [28] Yapa R.D., Harada K.; Breast Skin-Line Estimation and Breast Segmentation in Mammograms using Fast-Marching Method, International Journal of Biological and Life Sciences, 3(1), 2007.
• [29] Abubaker A., Aqel M., Qahwaji R. et al.; Average Row Thresholding Method for Mammogram Segmentation, The 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2005, pp. 3288-3291.
• [30] Ferrari R., Rangayyan R.; Automatic identication of the pectoral muscle in mammograms, IEEE Transactions on Medical Imaging, 23, 2004, pp. 232-245.
• [31] Mirzaalian H., Ahmadzadeh M.R., Kolahdoozan F.; Breast Contour Detection on Digital Mammogram, Proceedings of 2nd International Conference on Information and Communication Technologies: from Theory to Applications, 2006, pp. 1804-1808.
• [32] Masek M., Attikiouzel Y., deSilva C.J.S.; Skin-air interface extraction from mammograms using an automatic local thresholding algorithm, 15th Biennial International Conference Biosignal, 2000, pp. 204-206.
• [33] Bick U., Giger M.L., Schmidt R.A. et al.; Automated segmentation of digitized mammograms, Academic Radiology, 2, 1995, pp. 1-9.
• [34] Yin F., Giger M.L., Doi K. et al.; Computerized detection of masses in digital mammograms: analysis of bilateral subtraction images, Medical Physics, 18(5), 1991, pp. 955-963.
• [35] Abdel-Mottaleb M., Carman C.S., Hill C.R. et al.; Locating the boundary between the breast skin edge and the background in digitized mammograms, 3rd International Workshop on Digital Mammography, 98, 1996, pp. 467-470.
• [36] Bozek J., Grgic M., Mustra M.; Breast border extraction and pectoral muscle detection using wavelet decomposition, Proceedings of the International IEEE Conference EUROCON, 2009, pp. 1426-1433.