Abstract
This paper introduces entropy as a feature for 1D signals. It proposes the ratio between signal perturbation (i.e. its part within minimum and maximum grey level) and the total signal energy as a measurement of entropy. Linear transformation of 2D signals into 1D signals is also illustrated together with the results. This paper also presents the experimentation carried out on different mammograms containing different pathologies (microcalcification and masses).A comparison between different entropy measures and ours is also illustrated in this study.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
National Alliance of Breast Cancer Organization Facts About Breast Cancer in the USA, New York (1997)
Schmidt, R.A., et al.: Computerized Detection of Lesions Missed by Mammography. In: Proc. 3rd. Inter. Workshop on Digital Mammography, Chicago, IL (1996)
Kropinsky, E.A.: The Future of Image Perception in Radiology. Academic Radiology 10(1) (2003)
Mikula, K., Sarti, A., Sgallari, F.: Handbook of Medical Image Analysis: Segmentation and Registration Models. In: Suri, J., et al. (eds.), Marcel Dekker Inc., New York (2004)
Stavos, A., Thickman, D., Rapp, C., Dennis, M., Parker, S., Sisney, G.A.: Solid breast modules: Use of sonography to distinguish between benign and malignant lesions. Radiology 196, 123–134 (1995)
Jackson, V.: Management of solid breast modules: What is the role of sonography? Radiology 196, 14–15 (1995)
Arger, P., Sehgal, C., Conant, E., Zuckerman, J., Rowlìng, S., Patton, J.: Interreader variability and predictive value of is descriptions of solid masses: Pilot study. Acad. Radiol. 8, 335–342 (2001)
Madabhushi, A., Metaxas, D.N.: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. ZEEE Trans. Med. Imag. 22(2), 155–169 (2003)
Horsch, K., Giger, M.L., Venta, L.A., Vyborny, C.J.: Automatic segmentation of breast lesions on ultrasound. Med. Phys. 28(8), 1652–1659 (2001)
Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29(2), 157–164 (2004)
Horsch, K., Giger, M.L., Vyborny, C.J., Venta, L.A.: Performance of computer-aided diagnosis in the interpretation of lesions on breast sonography. Acad. Radiol. 11(3), 272–280 (2004)
Drukker, K., Giger, M.L., Horsch, K., Kupinski, C.J., Vyborny, M.A., Mendelson, E.B.: Computerized lesion detection on breast ultrasound. Med. Phys. 29(7), 1438–1446 (2002)
Drukker, K., Gìger, M.L., Vyborny, C.J., Mendelson, E.B.: Computerized detection and classification of cancer on breast ultrasound. Acad. Radiol. 11(5), 526–535 (2004)
Chen, D.R., Chang, R.F., Kuo, W.J., Chen, M.C., Huang, Y.L.: Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks. Ultrasound Med. Biol. 28(10), 1301–1310 (2002)
Huang, Y.L., Chen, D.R.: Watershed segmentation for breast tumor in 2-D sonography. UltrasoundMed. Biol. 30(5), 625–632 (2004)
Chen, D.R., Chang, R.F., Wu, W.J., Moon, W.K., Wu, W.L.: 3-D breast ultrasound segmentation using active contour model. Ultrasound Med. Biol. 29(7), 1017–1026 (2003)
Chang, R.-F., Wu, W.-J., Moon, W.K., Chen, W.M., Lee, W., Chen, D.-R.: Segmentation of breast tumor in three-dimensional ultrasound images using three-dimensional discrete active contour model. Ultrasound Med. Biol. 29(11), 1571–1581 (2003)
Chang, R.-F., Wu, W.-J., Tseng, C.-C., Chen, D.-R., Moon, W.K.: 3-D snake for ultrasound in margin evaluation for malignant breast tumor excision using mammotome. IEEE Trans. Inf. Tech. Biomed. 7, 197–201 (2003)
Chang, R.-F., Wu, W.-J., Moon, W.K., Chou, Y.H., Chen, D.-R.: Support vector Machines for diagnosis of Breast tumors on ultrasound images. Acad. Radiol. 10(2), 189–197 (2003)
Sahiner, B., Chan, H.P., Roubidoux, M.A., Helvie, M.A., Hadjiiski, L.M., Ramachandran, A., Paramagul, C., LeCarpentier, G.L., Nees, A., Blane, C.: Computerized characterization of breast masses on threedimensional ultrasound volumes. Med. Phys. 31(4), 744–754 (2004)
Doi, K., MacMahon, H., Katsuragawa, S., Nishikawa, R.M., Jiang, Y.: Computer-aided diagnosis in radiology: potential and pitfalls. Eur. J. Radiol. 31(2), 97–109 (1999)
Giger, M.L.: Overview of computer-aided diagnosis in breast imaging. In: Computer-aided Diagnosis in Medical Imaging, pp. 167–176. Elsevier, Amsterdam (1998)
Jiang, Y., et al.: Improving breast cancer diagnosis with computer-aided diagnosis. Acedemic radiol. 6, 22–23 (1999)
Huo, Z., et al.: Effectiveness of CAD in the diagnosis of breast cancer: an observer study on an independent database of mammograns. Radiology 7, 1077–1084 (2000)
Chan, H.P., et al.: Improvement of radiologists characterization of mammographic masses by using CAD: an ROC study. Radiology 212, 817–827 (1999)
Kundel, H.L., Nodine, C.F., Carmody, D.: Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol 13, 175–181 (1978)
Mello-Thoms: Perception of breast cancer: eye-position analysis of mammogram interpretation. Acad Radiol 10, 4–12 (2003)
Julesz, B.: A theory of Preattentive texture discriminant based on first-order statisics of texture. Biological Cybernetics 41, 131–138 (1981)
Casanova, A., Gesù, V.D., Bosco, G.L, Vitulano, S.: Entropy measures in image classification. In: 4rd International Workshop Hmp04: Human And Machine Perception (Santa Caterina di Pittinuri - Oristano), Italy (September 2004) ISBN 981-238-431-6
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Vitulano, S., Casanova, A. (2008). The Role of Entropy: Mammogram Analysis. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_86
Download citation
DOI: https://doi.org/10.1007/978-3-540-69812-8_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69811-1
Online ISBN: 978-3-540-69812-8
eBook Packages: Computer ScienceComputer Science (R0)