Application Of Mathematical Morphology Algorithm For Image Enhancement Of Breast Cancer Detection

Authors

  • Wiji Lestari Duta Bangsa University
  • Sri Sumarlinda Duta Bangsa University

DOI:

https://doi.org/10.47701/icohetech.v1i1.798

Keywords:

mathematical morphology, breast healthy, image enhancement, erosion, dilation

Abstract

This study aims to produce an image processing application using Mathematical Morphology to improve the quality of the digital image for breast cancer detection. Medical image is an image produced or used in the medical field. Improving medical image quality is very useful for diagnosis and advanced image processing. Breast healthy is important for women. Breast cancer is the main killer for women. Biomedical breast image data is secondary data. The next process is the initial processing, which is processing that is related to pixel size, gray scale, and so on. The improvement of medical image in this study uses the Mathematical Morphology method which consists of Dilation, Erosion, Opening (Erosion-Dilation) and Closing (Dilation-Erosion) processes. The expected results of this research are medical digital images that have improved their quality as a result of Dilation, Erosion, opening and closing processes.

References

C. P. Utomo, A.Kardiana and R. Yuliwulandari , “Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques”, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 3, No. 7, 2014.

Nadeem Tariq, “Breast Cancer Detection using Artificial Neural Networks”, J Mol Biomark Diagn 2017, 9:1.

World Health Organization, Breast Cancer, 2015

A. Alias and B.Paulchamy, “Detection of Breast Cancer Using Artificial Neural Networks”, International Journal of Innovative Research in Science, Engineering and Technology Vol. 3, Issue 3, 2014.

Sonal Naranje, “Early Detection of Breast Cancer using ANN”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 4, Issue 7, 2016.

F. Strand, E. Azavedo, R. Hellgren, K. Humphreys, M. Eriksson1, J. Shepherd, P.Hall and K. Czene, “Localized mammographic density is associated with interval cancer and large breast cancer: a nested case-control study”, Breast Cancer Research (2019) 21:8.

W. Yue , Z. Wang, H.Chen , A.Payne and X. Liu, “Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis”, Designs 2018, 2, 13.

S. Saini and R. Vijay, “Optimization of Artificial Neural Network Breast Cancer Detection System based on Image Registration Techniques”, International Journal of Computer Applications (0975 – 8887) Volume 105 – No. 14, 2014.

M. M. Mehdy, P. Y. Ng,1 E. F. Shair, N. I. Md Saleh, and C. Gomes (2017), “Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer”, Hindawi Computational and Mathematical Methods in Medicine, Volume 2017.

Palwinder Singh, “A Review On Role Of Mathematical Morphology In Digital Image Processing”, International Journal of Recent Trends in Engineering & Research (IJRTER) Volume 02, Issue 04, 2016.

Nikesh T. Gadare, and S. A. Ladhake, “Image Enhancement and Background Detection Using Morphological Transformation”, Int. Journal of Engineering Research and Applications , Vol. 4, Issue 2 ( Version 2), 2014.

D. R. Naya and A. Bho, “Image Enhancement Using Fuzzy Morphology”, Journal of Engineering, Computers & Applied Sciences (JEC&AS) Volume 3, No.3, 2014.

R. Firoz, M. S. Ali, M. N. U. Khan, M. K. Hossain, M. K Islam, M. Shahinuzzaman, “Medical Image Enhancement Using Morphological Transformation”, Journal of Data Analysis and Information Processing, 2016, 4, 1-12.

S. Singh and S. K. Grewal, “Role of Mathematical Morphology in Digital Image Processing: A Review”, International Journal of Scientific Engineering and Research (IJSER) Volume 2 Issue 4, 2014.

Downloads

Published

2019-11-16