Detection of Micro Calcifications in Mammogram Images Using Texture Analysis and Logistic Regression

Authors

DOI:

https://doi.org/10.18201/ijisae.2019457675

Keywords:

Breast Cancer, Classification, Image processing, Logistic Regression, Texture Analysis

Abstract

Micro-calcification in the breast is a symptom of breast cancer. Therefore, detection of micro-calcification in mammogram image plays an important role in the early diagnosis of breast cancer. Because the mammogram images are 2-dimensional, different tissues in the breast are seen on top of each other. Therefore, it is a compelling task for radiologists to identify the masses found in mammogram images. There are different methods for detecting micro-calcification in mammogram images. In this study, different image processing techniques were applied on mammogram images and a region of 80x80 pixel was taken from breast tissue. Texture features of this region were extracted using co-occurrence matrix and classified by logistic regression analysis. Classification success of 88% was achieved with the proposed model.

Downloads

Download data is not yet available.

References

J. B. Li, “Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis” Journal of Medical Systems, vol. 36, no. 4, pp. 2235-2244, 2012.

J. Ferlay, M. Colombet, I Soerjomataram, C. Mathers, D.M. Parkin, M. Pineros, A. Znaor, F. Bray, “Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods” International Journal of Cancer, vol. 144, no. 8, pp. 1941-1953, 2019.

J. G. Melekoodappattu and P.S. Subbian, “A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features” Journal of Medical Systems, vol. 43, no. 7, pp. 183, 2019.

V. Ramachandran and V. Kishorebabu, “A Tri- State Filter for the Removal of Salt and Pepper Noise in Mammogram Images” Journal of Medical Systems, vol. 43, no. 2, pp. 40, 2019.

A. Taliafico, G. Mariscotti, M. Durando, C. Stevanin, G. Tagliafico, L. Martino, B. Bignotti, M. Calabrese, N. Houssami, “Characterisation of microcalcification clusters on 2D digital mammography (FFDM) and digital breast tomosynthesis (DBT): does DBT underestimate microcalcification clusters? Results of a multicentre study” Eur Radiol, vol. 25, no. 1, pp. 9-14, 2015.

H. Cai, Q. Huang, W. Rong, Y. Song, J. Li, J. Wang, J. Chen, L. Li, “Breast Microcalcification Diagnosis Using Deep Convolutional Neural Network from Digital Mammograms” Journal of Computational and Mathematical Methods in Medicine, vol. 2019, no. 2717454, pp. 10, 2019.

M. Sharkas, M. Al-Sharkawy, D.A. Ragab, “Detection of Microcalcifications in Mammograms Using Support Vector Machine” UKSim 5th European Symposium on Computer Modeling and Simulation,16-18 Nov. 2011, doi: 10.1109/EMS.2011.23.

B. Kurt, V. Nabiyev, K. Turhan, “An Automated Computer-Aided Detection (CADe) And Diagnosis (CADx) System For Breast Microcalcifications In Mammograms” Selcuk Univ J Eng Sci Tech, vol. 6, no. 3, pp. 355-376.

T. M. A. Basile et al., “Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system” Physica Medica, vol. 64, no. 4, pp. 1-9, 2019.

J. Suckling, “The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica” Paper presented at the International Congress Series, 1994.

D. Vernon, “Machine Vision” Prentice-Hall, 1991.

K. Joung-Youn, K. Lee-Sup, H. Seung-Ho, “An advanced contrast enhancement using partially overlapped sub-block histogram equalization” IEEE Transactions on Circuits and Systems for Video Technology, vol. 11, no. 4, pp. 475-484, 2001.

M. Cui, S. Prasad, M. Mahrooghy, J. V. Aanstoos, M. A. Lee, L. M. Bruce, “Decision Fusion of Textural Features Derived From Polarimetric Data for Levee Assessment” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 3, pp. 970-976, 2012.

N. Severoğlu, “Mammogram images classification using Gray Level Co-occurence Matrices” 24th Signal Processing and Communication Application Conference, 16-19 May 2016, doi: 10.1109/SIU.2016.7496106.

H. Midi, S. K. Sarkar, S. Rana, “Collinearity diagnostics of binary logistic regression model” Journal of Interdisciplinary Mathematics, vol. 13, no. 3, pp. 253-267, 2010.

Downloads

Published

18.12.2019

How to Cite

Tutuncu, K., & Cataltas, O. (2019). Detection of Micro Calcifications in Mammogram Images Using Texture Analysis and Logistic Regression. International Journal of Intelligent Systems and Applications in Engineering, 7(4), 227–231. https://doi.org/10.18201/ijisae.2019457675

Issue

Section

Research Article