The week's pick
Random Articles
Reseach Article
An Automated Breast Cancer Recognition Application
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 173 - Number 9 |
| Year of Publication: 2017 |
| Authors: Maleika Heenaye- Mamode Khan, Bhoomita Motee |
10.5120/ijca2017915411
|
Maleika Heenaye- Mamode Khan, Bhoomita Motee . An Automated Breast Cancer Recognition Application. International Journal of Computer Applications. 173, 9 ( Sep 2017), 24-27. DOI=10.5120/ijca2017915411
Abstract
Breast cancer is one serious disease that is causing a high rate of mortality worldwide. Thus, it is critical to devise applications that can help in the early detection and diagnosis of this type of cancer as it spreads rapidly to the rest of the body. At present, mammography is the technique used to detect abnormalities in masses and calcifications in the breast. However, in the early stage, masses are very small and granular which makes early detection is challenging task. With the advances in the field of computer vision, image processing techniques can be applied on medical images to develop automated breast recognition system for early detection. This paper proposes an approach that uses texture description of images known as Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) to represent features in the breast. After the extraction of masses or calcification, LBP and HOG were applied on the image. Both methods showed a very high recognition rate compared to other existing breast cancer detection system
References
- Cheng H. D., Shi, X. J. , Min R., Hu., L. M, Cai, X. P. and. Du, H. N , 2006, “Approaches for automated detection and classification of masses in mammograms,” Pattern Recognition, vol. 39, no. 4, pp. 646–668.
- Amrutha, M., 2015, “Detection of Micro Calcifications in Ultrasound Images,” vol. 3, no. 2, pp. 905–910.
- Rajeshwari, M., Chenthilnathan, A., and Rama, K., 2014 “ A Review on Breast Cancer,” International Journal of Pharmacy and Biological Sciences, vol. 4, no. 2
- Kavitha, K. and Kumaravel, N. , 2007, “A comparitive study of various microCalcification cluster detection methods in digitized mammograms,” IWSSIP and EC-SIPMCS - Proc. 2007 14th Int. Workshop on Systems, Signals and Image Processing, and 6th EURASIP Conf. Focused on Speech and Image Processing, Multimedia Communications and Services, pp. 405–409
- Anand, S. and Rathana, R, V., 2013“Architectural Distortion Detection in Mammogram using Contourlet Transform and Texture Features,” International Journal of Computer Applications, vol. 74, no. 5, pp. 12–19.
- J. Bozek, E. Dumic, and M. Grgic, 2009, “Bilateral asymmetry detection in digital mammography using B-spline interpolation,”16th International Conference on Systems, Signals and Image Processing, IWSSIP 2009.
- Rangaraj, M., Rangayyana, B., Ayresa, F., Leo Desautelsa, J., 2007, A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs., Journal of the Franklin Institute 344 (2007) 312–348, ELsevier
- Eddaoudi, F., Regragui, F., Mahmoudi, A., and Lamouri, N., 2011 “Masses Detection Using SVM Classifier Based on Textures Analysis,” Applied Mathematical Sciences, vol. 5, no. 8, pp. 367–379.
- Chaabani, M, A.,Boujelben, A., Mahfoudhi, A., 2010“An Automatic-Pre-processing Method For Mammographic Images,” International Journal of Digital Content Technology and its Application, vol. 4, no. 3, pp. 190–201.
- Fallahi, A. and Jafari, S. 2011 “An Expert System for Detection of Breast Cancer Using Data Preprocessing and Bayesian Network,” International Journal of Advanced Science and Technology, vol. 34, pp. 65–70.
- Saheb Basha S, Satya Prasad K, 2009, “Automatic Detection of Breast Cancer Mass in Mammograms using Morphological Operators and Fuzzy C – Means Clustering”, Journal of Theoretical and Applied Information Technology, pp. 704-709
- Satoto, K, Nurhayati, O., and Isnanto, R., 2011, Indonesia, International Journal of Computer Science and Technology, IJCST Vol. 2, Issue 3.
- Mini, M. G and Thomas, T., 2003, “A neural network method for mammogram analysis based on statistical features,” IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 4, pp. 1489–1492.
- S. Shanthi, 2012 “Computer Aided System for Detection and Classification of Breast Cancer,” International Journal of Information Technology, Control and Automation, vol. 2, no. 4, pp. 87–98.
Index Terms
Keywords