CAD based Medical Image Processing: Emphasis to Breast Cancer Detection

G. R. Sinha*
Professor, Myanmar Institute of Information Technology, Mandalay, Myanmar.
Periodicity:October - December'2017


Medical Image Processing exploits the use of signal processing concept when applied to medical images. The medical images may be X-rays, Computed Tomographic (CT) images, or Mammograms. This paper gives an overview of image processing for the application areas of medical science that covers the concepts of Computer-Aided Diagnosis (CAD) system used in medical images and diagnosis system for segmentation, detection, and classification of cancer stages by post-processing the medical images. Medical Image Processing has brilliant research scope in understanding physical, mathematical, and engineering avenues of medical image uses in various disease diagnosis methods. This enables to “see” inside the human body to diagnose the disease and monitor treatment; an overview of recent developments in the field of medical imaging along with prominent challenges that radiologists and physicians come across while scanning, interpretation, and diagnosis processes. A practical approach and experimental results in some cases of segmentation with a review of a specific algorithm for medical image processing or analysis, along with the concept of CAD system and its evaluation criteria are discussed.


Breast Cancer, Medical Image Processing, Mammographic Image, Computer-Aided Diagnosis (CAD), Feature Extraction, Segmentation, Detection

How to Cite this Article?

Sinha, G. R. (2017). Cad Based Medical Image Processing: Emphasis to Breast Cancer Detection. i-manager’s Journal on Software Engineering, 12(2), 15-24.


[1]. An, J., Rousson, M., & Xu, C. (2007). G-convergence approximation to piecewise smooth medical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 495-502). Springer, Berlin, Heidelberg.
[2]. Breast Cancer Stages. (n.d.). In Retrieved from diagnosis/staging
[3]. Bresson, X., Esedoglu, S., Vandergheynst, P., Thiran, J. P., & Osher, S. (2007). Fast global minimization of the active contour/snake model. Journal of Mathematical Imaging and Vision, 28(2), 151-167.
[4]. Bruce, L. M., & Adhami, R. R. (1999). Classifying mammographic mass shapes using the wavelet transform modulus-maxima method. IEEE Transactions on Medical Imaging, 18(12), 1170-1177.
[5]. Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266-277.
[6]. Cheng, H. D., Shan, J., Ju, W., Guo, Y., & Zhang, L. (2010). Automated breast cancer detection and classification using ultrasound images: A survey. Pattern Recognition, 43(1), 299-317.
[7]. Cheng, H. D., Shi, X. J., Min, R., Hu, L. M., Cai, X. P., & Du, H. N. (2006). Approaches for automated detection and classification of masses in mammograms. Pattern Recognition, 39(4), 646-668.
[8]. Cremers, D., Schmidt, F. R., & Barthel, F. (2008). Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on (pp. 1-6). IEEE.
[9]. d'Angelo, P., Wöhler, C., & Krüger, L. (2006). Model Based multi-view Active Contours for Quality Inspection. In Computer Vision and Graphics (pp. 565-574). Springer, Dordrecht.
[10]. Durbin, R., Szeliski, R., & Yuille, A. (1989). An analysis of the elastic net approach to the traveling salesman problem. Neural Computation, 1(3), 348-358.
[11]. Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37.
[12]. Kowal, M., Filipczuk, P., Obuchowicz, A., & Korbicz, J. (2011). Computer-aided diagnosis of breast cancer using Gaussian mixture cytological image segmentation. Journal of Medical Informatics & Technologies, 17, 257- 262.
[13]. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22(1), 79-86.
[14]. Lankton, S., & Tannenbaum, A. (2008). Localizing region-based active contours. IEEE Transactions on Image Processing, 17(11), 2029-2039.
[15]. Leventon, M. E., Grimson, W. E. L., & Faugeras, O. (2000). Statistical shape influence in geodesic active contours. In Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on (Vol. 1, pp. 316- 323). IEEE.
[16]. Li, C., Kao, C. Y., Gore, J. C., & Ding, Z. (2008). Minimization of region-scalable fitting energy for image segmentation. IEEE Transactions on Image Processing, 17(10), 1940-1949.
[17]. Mumford, D., & Shah, J. (1985). Boundary detection by minimizing functionals. In IEEE Conference on Computer Vision and Pattern Recognition (Vol. 17, pp. 137-154). IEEE.
[18]. Mumford, D., & Shah, J. (1989). Optimal approximations by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics, 42(5), 577-685.
[19]. Oliver i Malagelada, A. (2007). Automatic mass segmentation in mammographic images (Doctoral Dissertation, Universitat de Girona).
[20]. Pagonis, D. C., & Sidiropoulos, K. (2010). Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging. e-Journal of Science & Technology (e-JST), 2(5), 33-39.
[21]. Patel, B. C., & Sinha, G. R. (2010a). An adaptive km eans clustering algorithm for breast image segmentation. International Journal of Computer Applications, 10(4), 35-38.
[22]. Patel, B. C., & Sinha, G. R. (2010b). Early detection of breast cancer using Self similar fractal method. International Journal of Computer Applications, 10(4), 39-43.
[23]. Patel, B. C., & Sinha, G. R. (2010c). Structural analysis of tissue in contiguous micro-calcifications in mammograms for breast cancer identification. i-manager's Journal on Future Engineering and Technology, 6(2), 20-27.
[24]. Patel, B. C., & Sinha, G. R. (2011a). Comparative performance evaluation of segmentation methods in breast cancer images. International Journal of Machine Intelligence, 3(3), 130-133.
[25]. Patel, B. C., & Sinha, G. R. (2011b). Mammographic image analysis method for early detection of breast cancer. i-manager's Journal on Future Engineering and Technology, 7(1), 10-16.
[26]. Patel, B. C., & Sinha, G. R. (2012). Energy and Region based Detection and Segmentation of Breast Cancer Mammographic Images. International Journal of Image, Graphics and Signal Processing, 4(6), 44-51.
[27]. Patel, B. C., & Sinha, G. R. (2014a). Efficient detection of suspected areas in mammographic breast cancer images. i-manager's Journal on Pattern Recognition, 1(4), 1-10.
[28]. Patel, B. C., & Sinha, G. R. (2014b). Mammography feature analysis and mass detection in breast cancer images. In Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on (pp. 474-478). IEEE.
[29]. Patel, B. C., Sinha, G. R., & Thakur, K. (2011). Early detection of breast cancer using a modified topological derivative based method. Int. J. Pure Appl. Sci. Technol, 7(1), 75-80.
[30]. Patel, B. C., Sinha, G. R., & Thakur, K. (2013). Mass segmentation and Feature extraction of Mammographic Images of Breast cancer in Computer-aided diagnosis (CAD) System. CSVTU Journal of Research, 67-74.
[31]. Rangayyan, R. M., Ayres, F. J., & Desautels, J. L. (2007). A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs. Journal of the Franklin Institute, 344(3-4), 312-348.
[32]. Sampat, M. P., Markey, M. K., & Bovik, A. C. (2005). Computer - aided detection and diagnosis in mammography. Handbook of Image and Video Processing, 2(1), 1195-1217.
[33]. Shah, J. (1994). Piecewise smooth approximations of functions. Calculus of Variations and Partial Differential Equations, 2(3), 315-328.
[34]. Singh, S., & Gupta, P. R. (2011). Breast cancer detection and classification using neural network. International Journal of Advanced Engineering Sciences and Technologies, 6(1), 4-9.
[35]. Sinha G. R., & Patel B. C. (2014). Medical Image Processing: Concepts and Applications. PHI Learning Private Limited.
[36]. Sinha, G. R. (2015). Fuzzy based Medical Image Processing. In Kumar, A. V. S. (Eds.). Fuzzy Export System for Disease Diagnosis (pp. 45-61). IGI Global Publishers, USA.
[37]. Zhen, L., & Chan, A. K. (2001). An artificial intelligent algorithm for tumor detection in screening mammogram. IEEE Transactions on Medical Imaging, 20(7), 559-567.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.