Abstract
Microcalcifications have been mainly targeted as the earliest sign of breast cancer, thus their early detection is very important process. Since their size is very small and sometimes hidden by breast tissue, computer-based detection output can assist the radiologist to increase the diagnostic accuracy. This paper presents a research on mammography images using rough entropy and fuzzy approach. Our proposed method includes two main steps; preprocessing and segmentation. In the first step, we have implemented mammography image enhancement using wavelet transform, CLAHE and anisotropic diffusion filter then rough pectoral muscle extraction for false region reduction and better segmentation. In the second step, we have used Rough entropy to define a threshold and then, fuzzy based microcalcification enhancement, after these microcalcifications have been segmented using an iterative detection algorithm. By the combination of these methods, a novel hybrid algorithm has been developed and successful results have been obtained on MIAS database.
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
References
Guan, Q., Zang, J., Chen, S., Pokropek, A.T.: Automatic Segmentation of Micro-caicification Based on SIFT in Mammograms. In: International Conference on BioMedical Engineering and Informatics, pp. 13–17. IEEE Press, New York (2008)
Mammographic Image Analysis Society, http://peipa.essex.ac.uk/info/mias.html
Kurt, B., Nabiyev, V.V., Turhan, K.: Contrast Enhancement and Breast Segmentation of Mammograms. In: 2nd World Conference on Information Technology (2011)
Otsu, N.: A Threshold Selection Method from Gray-level Histograms. IEEE Trans. on Systems, Man & Cybernatics 9, 62–66 (1979)
Deepa, S., Bharathi, S.: Efficient ROI Segmentation of Digital Mammogram Images using Otsu’s N Thresholding Method. Int. J. of Engineering Research & Technology 2 (2013)
Kurt, B., Nabiyev, V.V., Turhan, K.: Medical Images Enhancement by using Anisotropic Filter and CLAHE. In: International Symposium on Innovations in Intelligent Systems and Applications, pp. 1–4. IEEE Press, New York (2012)
Bird, R.G., Wallace, T.W., Yankaskas, B.C.: Analysis of Cancers Missed at Screening Mammography. Radiology 184, 613–617 (1992)
Kumar, S.V., Lazarus, M.N., Nagaraju, C.: A Novel Method for the Detection of Microcalcifications Based on Multi-scale Morphological Gradient Watershed Segmentation Algorithm. Int. J. of Engineering Science and Technology 2, 2616–2622 (2010)
Mohanalin, L., Kalra, P.K., Kumar, N.: An Automatic Method to Enhance Microcalcifications using Normalized Tsallis Entropy. Signal Processing 90, 952–958 (2010)
Balakumaran, T., Vennila, I.L.A., Shankar, C.G.: Detection of Microcalcification in Mammograms using Wavelet Transform and Fuzzy Shell Clustering. Int. J. of Computer Science and Information Security 7, 121–125 (2010)
Pal, S.K., Shankar, U., Mitra, P.: Granular Computing, Rough Entropy and Object Extraction. Pattern Recognition Letters 26, 2509–2517 (2005)
Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds.): RSCTC 2010. LNCS, vol. 6086. Springer, Heidelberg (2010)
Mohanalin, J., Beenamol, Kalra, P.K., Kumar, N.: A Novel Automatic Microcalcification Detection Technique using Tsallis Entropy & Type II Fuzzy Index. Computers and Mathematics with Applications 60, 2426–2432 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kurt, B., Nabiyev, V.V., Turhan, K. (2013). Automatic Microcalcification Segmentation Using Rough Entropy and Fuzzy Approach. In: Bursa, M., Khuri, S., Renda, M.E. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2013. Lecture Notes in Computer Science, vol 8060. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40093-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-40093-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40092-6
Online ISBN: 978-3-642-40093-3
eBook Packages: Computer ScienceComputer Science (R0)