Open Access Open Access  Restricted Access Subscription or Fee Access

A Review on Different Techniques Used for Feature Extractions from Mammogram of Breast Cancer

Hiral N. Pokar, Poorvi H. Patel


Breast cancer detection is still a complex and challenging problem. Mammogram contains low signal-to-noise ratio (SNR) and a complex structured background so identifying cancer tissues using this technique is a time consuming task even for expert radiologists. Therefore, there is still a need to enhance imaging in digital mammogram, where enhancement in medical imaging is the use of computers to make the image clearer. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image processing and artificial intelligence-based solutions for that task can be of great realistic significance. This paper discusses about different techniques used to scan the whole mammogram and perform filtering, segmentation, and features extraction.


Mammography, image enhancement, segmentation, Region of Interest (ROI), Microcalcification

Full Text:



Radiological Society of North America. Breast Cancer. Available from:

Delp EJ. Digital Mammography Research at Purdue University. Available from:

Dakovic M, Mijovic S. Basic Feature Extractions from Mammograms. Proceedings of the IEEE 2012 Mediterranean Conference on Embedded Computing (MECO); 2012 June 19–21; Bar.

Paramkusham S, Rao KMM, Prabhakar Rao BVVSN. Early Stage Detection of Breast Cancer Using Novel Image Processing Techniques, Matlab and Labview Implementation. Proceedings of the IEEE 15th International Conference on Advanced Computing Technologies (ICACT); 2013 Sep 21–22; Rajampet, A.P., India. 1–5p.

Aichinger H, Dierker J, Barfuß SJ, et al. Radiation Exposure and Image Quality in X-Ray Diagnostic Radiology. Heilderberg: Springer-Verlag; 2004.

Spandana P, Rao KMM, Prabhakar Rao BVVSN, et al. Novel Image Processing Techniques for Early Detection of Breast Cancer, Mat lab and Lab view implementation. IEEE Point-of-Care Healthcare Technologies (PHT); 2013 Jan 16–18; Bangalore, India.

Fathima MM, Manimegalai D, Thaiyalnayaki S. Automatic Detection of Tumor Subtype in Mammograms Based on GLCM and DWT Features Using SVM. Proceedings of the IEEE 2013 International Conference on Information Communication and Embedded Systems (ICICES); 2013 Feb 21–22; Chennai, India.

Rejani YIA, Selvi ST. Early Detection of Breast Cancer Using Svm Classifier Technique. Int J Comp Sci Engin. 2009; 1(3): 127–30p.

Quintanilla-Dominguez J, Cortina-Januchs MG, Ojeda-Magana B, et al. Microcalcifications Detection Applying Artificial Neural Networks and Mathematical morphology in Digital Mammograms. Proceedings of the World Automation Congress in Signal & Communication; 2010 Sep 19–23; Kobe, Japan.

Kadhim DA. Development algorithm-computer program of digital mammograms Segmentation for detection of masses breast using Marker-Control Watershed in MATLAB environment. Journal of Karbala University. 2012; 1: 114–23p.

Fu KS, Mui JK. A Survey of Image Segmentation. Pattern Recog.1981; 13(1): 3–16p. Available from:

Vincent L, Soille P. Watersheds in Digital Spaces: An Efficient Algorithm based on Immersion Simulations. IEEE Trans Pattern and Machine Intell. 1991; 13: 583–98p. Available from:

Linguraru MG, Brady M, Yam M. Detection of Micro-calcifications using SMF. In: Peitgen HO (Eds.). Digital Mammography. Berlin-Heidelberg: Springer-Verlag; 2003. Available from: http://www

Heucke L, Knaak M, Orglmeister R. A New Image Segmentation Method Based on Human Brightness Perception and Foveal Adaptation. IEEE Signal Processing Letters. 2000; 7 (6): 129–31p.

Highnam RP, Brady JM, English R. Detecting Film-Screen Artifacts in Mammography using a Model-Based Approach. IEEE Transactions in Medical Imaging. 1999; 18: 1016–24p.

Evans CJ. Detecting and Removing Curvilinear Structures from Mammograms. Internal Report. UK: Department of Engineering Science, University of Oxford; 2001.

Yao Y. Segmentation Of Breast Cancer Mass In Mammograms And Detection Using Magnetic Resonance Imaging. Singapore: School of Electrical & Electronic Engineering, Nanyang Technological University; 2004: 561–567p.

Wang TC, Karayiannis NB. Detection of Microcalcifications in Digital Mammograms Using Wavelets. IEEE Transactions on Medical Imaging. 1998; 17 (4): 498–509p.

Wang H, Huang LL, Zhao XJ. Automated Detection of Masses in Digital

Mammogram Based on Pyramid. Proceedings of the IEEE International Conference on Wavelet Analysis and Pattern Recognition; 2007 Nov 2–4; Beijing, China.

Singh N, Mohapatra AG, Rath BN, et al. GUI Based Automatic Breast Cancer Mass and Calcification Detection in Mammogram Images using K-means and Fuzzy C-means Methods. International Journal of Machine Learning and Computing. 2012; 2 (1): 7–12p.

Bozek J, Dumic E, Grgic M. Bilateral Asymmetry Detection in Digital Mammography Using B-spline Interpolation. Proceedings of the 16th IEEE International Conference on Systems, Signals and Image Processing; 2009 June 18–20; Chalkida.1–4p.

Eltonsy NH, Elmaghraby AS, Tourassi GD. Bilateral Breast Volume Asymmetry in Screening Mammograms as a Potential Marker of Breast Cancer: Preliminary Experience. Proceedings of the IEEE International Conference on Image Processing; 2007 Sep 16–Oct 19; San Antonio, TX.

Katariya RN, Forest APM, Gravelle IH. Breast volumes in cancer of the breast. Br J Cancer. 1974; 29: 270–73p.


  • There are currently no refbacks.

This site has been shifted to