Abstract
An intensity modification based segmentation of microcalcifications from digital mammogram is presented in this paper. The proposed technique projects a novel enhancement method for mammogram images using BlackTop-Hat Transformation and Gauss Distribution as thresholding determinants, taking the neighbouring pixels into consideration for image segmentation. Further, the results are validated with MIAS database description and proved to produce the exact results complying with the descriptions given in the MIAS.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Rafael Gonzalez, C., Richard Woods, E.: Digital Image Processing, 2nd edn. Pearson Education, New Delhi (2002)
Shanmugavadivu, P., Lakshmi Narayanan, S.G.: Behavioural Analysis of FCM Clustering on Mammogram Images. In: Proceedings of the National Conference on Signal and Image Processing, pp. 175–177 (2012)
Shanmugavadivu, P., Lakshmi Narayanan, S.G.: Segmentation of Microcalcifications in Mammogram Images using Intensity - Directed Region Growing. In: Proceedings of the International Conference on Computer Communication and Informatics ICCCI 2013 (2013)
Shanmugavadivu, P., Lakshmi Narayanan, S.G.: Detection of Microcalcifications in Mammogram using Statistical Measures based Region Growing. In: SPIE Proceedings of the International Conference on Communication and Electronics System Design ICESD 2013, vol. 8760, pp. 87601M–1–6 (2013)
Shanmugavadivu, P., Lakshmi Narayanan, S.G.: Segmentation of Microcalcification Regions in Digital Mammograms using Self-Guided Region Growing. In: Proceedings of the International Conference on Emerging Trends in Science Engineering and Technology INCOSET 2012, pp. 274–279 (2012)
Nevine, H., Eltonsy, G.D., Tourassi, A.S.: A Concentric Morphology Model for the Detection of Masses in Mammography. IEEE Transactions on Medical Imaging 26, 880–889 (2007)
Singh, S., Bovis, K.: An Evaluation of Contrast Enhancement Techniques for Mammographic Breast Masses. IEEE Transactions on Information Technology in Biomedicine 9, 109–119 (2005)
Cahoon, T.C., Sutton, M.A., Bezdek, J.C.: Breast Cancer Detection using Image Processing Techniques 2, 973–976 (2000)
Pfisterer, R., Aghdasi, F.: Detection of Masses in Digitised Mammograms. In: Proceedings of the South African Symposium on Communications and Signal Processing, pp. 115–120. IEEE Digital Library (1998)
Chandrasekhar, R., Attikiouzel, Y.: New range-based neighbourhood operator for extracting edge and texture information from mammograms for subsequent image segmentation and analysis. In: IEE Proceedings - Science, Measurement and Technology, vol. 147, pp. 408–413. IEEE Digital Library (2000)
MIAS database, UK
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Shanmugavadivu, P., Narayanan, S.G.L. (2013). Segmentation of Microcalcifications in Digital Mammogram Images Using Intensity Modified BlackTop-Hat Transformation and Gauss Distribution. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-03844-5_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
Online ISBN: 978-3-319-03844-5
eBook Packages: Computer ScienceComputer Science (R0)