Abstract
This work analyzes the application of the semivariogram function to the characterization of breast tissue as malignant or benign in mammographic images. The method characterization is based on a process that selects, using stepwise technique, from all computed semivariance which best discriminate between the benign and malignant tissues. Then, a multilayer perceptron neural network is used to evaluate the ability of these features to predict the classification for each tissue sample. To verify this application we also describe tests that were carried out using a set of 117 tissues samples, 67 benign and 50 malignant. The result analysis has given a sensitivity of 92.8%, a specificity of 83.3% and an accuracy above 88.0%, which means encouraging results. The preliminary results of this approach are very promising in characterizing breast tissue.
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References
(AMS), A.C.S.: Learn about breast cancer (2006), Available at: http://www.cancer.org
(NCI), N.C.I.: Cancer stat fact sheets: Cancer of the breast (2006), Available at: http://seer.cancer.gov/statfacts/html/breast.html
Jiang, Y., Nishikawa, R.M., Schmidt, R.A., Toledano, A.Y., Doi, K.: Potential of computer-aided diagnosis to reduce variability in radiologists interpretations of mammograms depicting microcalcifications. Radiology 220, 787–794 (2001)
Zhang, P., Verma, B., Kumar, K.: Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pattern Recognition Letters 26, 909–919 (2005)
Christoyianni, A.K., Dermatas, E., Kokkinakis, G.: Computer aided diagnosis of breast cancer in digitized mammograms. Computerized Medical Imaging and Graphics 26, 309–319 (2002)
Thangavel, K., Karnan, M.: Computer aided diagnosis in digital mammograms: Detection of microcalcifications by meta heuristic algorithms. GVIP 5 (2005)
Sampat Mehul, P., Markey, M.K., Bovik, A.C.: Handbook of Image and Video Processing, p. 891. Academic Press, London (2005)
Ferreyra, R.A., Apezteguia, H.P., Sereno, R., Jones, J.W.: Reduction of soil water spatial sampling density using scaled semivariograms and simulated annealing. Geoderma 110, 265–289 (2002)
Goodin, D.G., Henebry, G.M.: Variability of spectral reflectance and vegetation indices in tallgrass prairie: Spatio-temporal analysis using semivariograms and close-range remote sensing. In: Geoscience and Remote Sensing Symposium Proceedings, IEEE International, vol. 2, pp. 825–827 (1998)
Carr, J.R., de Miranda, F.P.: The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE Transactions on Geoscience and Remote Sensing 36(6), 1945–1952 (1998)
Silva, A.C., Carvalho, P.C.P., Gattass, M.: Analysis of spatial variability using geostatistical functions for diagnosis of lung nodule in computerized tomography images. Pattern Analysis & Applications 7, 227 (2004)
Silva, A.C., Carvalho, P.C.P., Gattass, M.: Diagnosis of lung nodule using semivariogram and geometric measures in computerized tomography images. Computer Method and Programs in Medicine 79, 31–38 (2005)
Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C.: The mammographic images analysis society digital mammogram database. Exerpta Medical 1069, 375–378 (1994)
Clark, I.: Practical Geostatistics. Applied Sience Publishers, London (1979)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley-Interscience Publication, New York (1973)
Michie, D., Spiegelhalter, D.J., Taylor, C.C.: Machine Learning, Neural and Statistical Classification. Ellis Horwood Series in Artificial Intelligence, NJ, USA (1994)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1999)
StatSoft: Neural networks software (2006), Available at: http://www.statsoft.com/products/stat_nn.html
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da Silva, V.R., de Paiva, A.C., Silva, A.C., de Oliveira, A.C.M. (2006). Semivariogram Applied for Classification of Benign and Malignant Tissues in Mammography. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4142. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867661_51
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DOI: https://doi.org/10.1007/11867661_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44894-5
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