Call for Paper - November 2023 Edition
IJCA solicits original research papers for the November 2023 Edition. Last date of manuscript submission is October 20, 2023. Read More

Improved Region Growing based Breast Cancer Image Segmentation

Print
PDF
 
Download Full Text
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 135 - Number 8
Year of Publication: 2016
Authors:
Ibrahim Mohamed Jaber Alamin, W. Jeberson, H.K. Bajaj
10.5120/ijca2016908244

Ibrahim Mohamed Jaber Alamin, W Jeberson and H K Bajaj. Article: Improved Region Growing based Breast Cancer Image Segmentation. International Journal of Computer Applications 135(8):1-4, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Ibrahim Mohamed Jaber Alamin and W. Jeberson and H.K. Bajaj},
	title = {Article: Improved Region Growing based Breast Cancer Image Segmentation},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {135},
	number = {8},
	pages = {1-4},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Image segmentation is vital part for any pattern recognition applications like medical imagine for disease detection, biometric recognition etc. Accurate image segmentation leads to accurate method for particular application purpose. In this paper, novel robust and efficient segmentation method introduced for breast cancer image segmentation for early detection of disease. Our main research is to present framework for automatic and accurate diagnostic method for early detection of breast cancer. For any detection process, there are three main phases such as segmentation, feature extraction and detection (recognition). Feature extraction and detection is out of scope of this paper. This paper is focusing and evaluating the new proposed segmentation method. Before segmentation, we first performed the preprocessing step in order to remove the internal noises and getting smoother image. In preprocessing, first image is resized to 256 * 256 in standard size, and then RGB image is converted to grayscale. Grayscale image is filtered using Laplacian and average filters for noise removal. The preprocessed image is given input to segmentation method. Segmentation method proposed is this paper is based on existing region growing method. For breast cancer image segmentation, improved region growing method is introduced in this paper. This improved segmentation method considering constrain of orientation along with existing intensity constrain.

References

  1. Brzakovic, D., Luo, X.M., Brzakovic, P.: An approach to automated detection of tumors in mammograms. IEEE Transactions on Medical Imaging 9(3), 233–241 (1990).
  2. Li, H.D., Kallergi, M., Clarke, L.P., Jain, V.K., Clark, and R.A.: Markov Random Field for Tumor Detection in Digital Mammography. IEEE Transactions on Medical Imaging 14(3), 565–576 (1995)
  3. Li, L.H., and Qian, W., Clarke, L.P., Clark, R.A., Thomas, and J.: Improving Mass Detection by Adaptive and Multi-Scale Processing in Digitized Mammograms. Proceedings of SPIE—the International Society for Optical Engineering 3661 1, 490–498 (1999)
  4. Székely, N., Tóth, N., Pataki, B.: A Hybrid System for Detecting Masses in Mammographic Images. IEEE Transactions on Instrumentation and Measurement 55(3), 944–951 (2006)
  5. Zheng, B., Mello-Thoms, C., Wang, X.H., Gur, D.: Improvement of Visual Similarity of Similar Breast Masses Selected by Computer-Aided Diagnosis Schemes. In: 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007, April 12-15, pp. 516–519 (2007)
  6. Pappas, T.N.: An Adaptive Clustering Algorithm for Image Segmentation. IEEE Transactions on Signal Processing 40(4), 901–914 (1992)
  7. Sahiner, B., Hadjiiski, L.M., Chan, H.P., Paramagul, C., Nees, A., Helvie, M., Shi, J.: Concordance of Computer- Extracted Image Features with BI-RADS Descriptors for Mammographic Mass Margin. In: Giger, M.L., Karssemeijer, N. (eds.) Proc. of SPIE Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915 (2008)
  8. Rangayyan, R.M.: Biomedical Image Analysis. CRC Press LLC, Boca Raton (2005)
  9. Fauci, F., Bagnasco, S., Bellotti, R., Cascio, D., Cheran, S.C., De Carlo, F., De Nunzio, G., Fantacci, M.E., Forni, G., Lauria, A., Torres, E.L., Magro, R., Masala, G.L., Oliva, P., Quarta, M., Raso, G., Retico, A., Tangaro, S.: Mammogram Segmentation by Contour Searching and Massive Lesion Classification with Neural Network. In: 2004 IEEE Nuclear Science Symposium Conference Record, Rome, Italy, October 16–22, vol. 5, pp. 2695–2699 (2004)
  10. Petrick, N., Chan, H.P., Sahiner, B., Wei, D.: An Adaptive Density Weighted Contrast Enhancement Filter for Mammographic Breast Mass Detection. IEEE Transactions on Medical Imaging 15(1), 59–67 (1996)
  11. Zou, F., Zheng, Y., Zhou, Z., Agyepong, K.: Gradient Vector Flow Field and Mass Region Extraction in Digital Mammograms. In: 21st IEEE International Symposium on Computer-Based Medical Systems, CMBS 2008, Jyvaskyla, June 17-19, pp. 41– 43 (2008).

Keywords

Breast Cancer, Preprocessing, Segmentation, Region Growing, Noise Removal, Filtering, Orientation.