Differentiation between Normal and Abnormal Cases by Maximum Frequencies of Images of Breast Tissues

  • Salim J. Attia Department of Basic Sciences, College of Dentistry, University of Baghdad, Iraq
  • Ziad M. Abood Department of Physics, College of Education, Al- Mustansiryah University of Baghdad, Iraq
  • Ibrahim R. Agool Department of Physics, College of Science, Al- Mustansiryah University of Baghdad, Iraq
Keywords: Histogram, Maximum Frequency, Breast tissue and Digital Image


This study focuses on detection of the abnormality of various digital images taken from breast tissues and applying of maximum frequency calculation. It is found that this method gave good result to get the goal of research. The images were calculated for comparing between normal images and abnormal images by maximum values that each cells image reach to. Collection of 100 images is chosen to apply this method. Many research deal with this state [1][2][3][4].



(1) Nuryanti Mohd. Salleh, Harsa Amylia Mat Sakim and Nor Hayati Othman, "Neural Networks to Evaluate Morphological Features for Breast Cells Classification", International Journal of Computer Science and Network Security, Vol.8, No.9, pp. 51-58, September 2008.

(2) Lukasz Jelen , Thomas Fevens and Adam Krzyzak, "Classification of breast cancer malignancy using cytological images of fine needle aspiration biopsies", Int. J. Appl. Math. Comput. Sci., Vol. 18, No. 1, pp. 75–83, 2008.

(3) Dundar, M.M., "Computerized Classification of Intraductal Breast Lesions Using Histopathological Images", Biomedical Engineering, IEEE Transactions, Vol. 58, Issue 7, pp. 1977 – 1984, 2011.

(4) Tatari F, et. al, 2012, “Fuzzy-probabilistic multi agent system for breast cancer risk assessment and insurance premium assignment”, J. Biomed. Inform., 2012.

(5) Margie Patlak, Sharyl J. Nass, I. Craig Henderson, and Joyce C. Lashof, “Mammography and Beyond: Developing Technologies for the Early Detection of Breast cancer”, National Academy Press Washington D.C., 2001.

(6) John C. Russ, “The Image Processing Handbook”, Third Edition, CRC Press LLC, 1998.

(7) Jonathan M. Blackledge and Dmitry A. Dubovitskiy, "A Surface Inspection Machine Vision System that Includes Fractal Texture Analysis", ISAST Transactions On Electronics And Signal Processing, 2008.

(8) Qiang Wu, Fatima A. Merchant and Kenneth R. Castleman, "Microscope Image Processing", Elsevier Inc. 2008.

How to Cite
Attia, S. J., Abood, Z. M., & Agool, I. R. (2019). Differentiation between Normal and Abnormal Cases by Maximum Frequencies of Images of Breast Tissues. Advances in Image and Video Processing, 7(3), 08-11. https://doi.org/10.14738/aivp.73.5125