Segmentation of Breast Tumor from Mammographic Images Using Histogram Peak Slicing Threshold

Authors

  • Dutta P M.Sc in Computer Science Dept. Of Computer Sc. &App University Of North Bengal
  • Sarkar K Research Personal Dept. Of Computer Sc. &App University Of North Bengal
  • Mandal A Assistant Professor Dept. Of Computer Sc. &App University Of North Bengal

Keywords:

Mammogram, Breast Cancer, Histogram Peak Slicing, Histogram Thresholding

Abstract

Medical image processing is a huge and challenging research field. Cancer of the breast is the most common among women in world wide. Mammography is a effectivediagnostic and screening tool to detect breast cancer at early stage. Mammograms use doses of ionizing radiation to create images like all X-rays. These images are then analyzed for any abnormal findings. Multiple research studies have been developed to improve cancer detection,diagnosis and evaluation.Over the last decade there has been a marked increased in the use of mammography to detect breast cancer. Various segmentation techniques have been used for detection of breast tumor from mammographic image in last decade. In this paper a method has been proposed based on histogram segmentation to detect the breast cancer from Mammographic images. The whole procedure has been done in MATLAB.

References

G.BharathaSreeja, Dr. P. Rathika, Dr. D. Devaraj“Detection of Tumours in Digital Mammograms Using Wavelet Based Adaptive Windowing Method”I.J.Modern Education and Computer Science, Vol.(03), pp. 57-65 April 2012

http://seer.cancer.gov/statfacts/html/breast.html [Accessed on 15/01/2016].

http://www.nationalbreastcancer.org/breast-cancer-diagnosis [Accessed on 15/01/2016].

http://breast-cancer.ca/abnorm-mams[Accessed on 15/01/2016]

Kai-yang Li, Zheng Dong, “A Novel Method of Detecting Calcifications from Mammogram Images Based on Wavelet and Sobel Detector,” Mechatronics and Automation, Proceedings of the 2006 IEEE International Conference, pp.1503 – 1508, June 2006.

Diyana, W.M., Besar, R., “Methods for clustered microcalcifications detection in digital mammograms,” ISSPIT,Vol.(02), pp.99 - 104 Dec.2004.

Songyang Yu, Ling Guan, “A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films” IEEE Transactions on Medical Imaging, Vol.( 19(02)), pp. 115 - 126, Feb. 2000

Auephanwiriyakul, S., Attrapadung, S., Thovutikul, S., Theera- Umpon N., “Breast Abnormality Detection in Mammograms Using Fuzzy Inference System,” Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference , pp. 155-160, May.2005.

https://en.wikipedia.org/wiki/Image_noise[Accessed on 14/01/2016]

KanishkaSarkar, ArdhenduMandal and Rakesh Kumar Mandal, “Brain Tumor Detection from T1 Weighted MRI Using Histogram Peak Difference Threshold”, Proc. of National Conference on Research Trends in Computer Science and Application (NCRTCSA-2015),pp.32-37, Nov. 07, 2015

Downloads

Published

2025-11-11

How to Cite

[1]
P. Dutta, K. Sarkar, and A. Mandal, “Segmentation of Breast Tumor from Mammographic Images Using Histogram Peak Slicing Threshold”, Int. J. Comp. Sci. Eng., vol. 4, no. 1, pp. 85–92, Nov. 2025.

Issue

Vol. 4 No. 1 (2016): IJCSE Special Issue February Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.