Acta Scientific Computer Sciences

Research Article Volume 3 Issue 11

Breast Cancer Classification Using: Pixel Interpolation

Osama Rezq Shahin*, Hamdy Mohammed Kelash, Gamal Mahrous Attiya and Osama Slah Farg Allah

Department of Computer Sciences, Jouf University, Saudi Arabia

*Corresponding Author: Osama Rezq Shahin, Department of Computer Sciences, Jouf University, Saudi Arabia.

Received: October 14, 2021; Published: October 28, 2021

Abstract

Image Processing represents the backbone research area within engineering and computer science specialization. It is promptly growing technologies today, and its applications founded in various aspects of biomedical fields especially in cancer disease. Breast cancer is considered the fatal one of all cancer types according to recent statistics all over the world. It is the most commonly cancer in women and the second reason of cancer death between females. About 23% of the total cancer cases in both developing and developed countries. In this work, an interpolation process was used to classify the breast cancer into main types, benign and malignant. This scheme dependent on the morphologic spectrum of mammographic masses. Malignant tumors had irregular shape percent higher than the benign tumors. By this way the boundary of the tumor will be interpolated by additional pixels to make the boundary smoothen as possible, these needed pixels is proportional with irregularity shape of the tumor, so that the increasing in interpolated pixels meaning the tumor goes toward the malignant case. The proposed system is implemented using MATLAB programming and tested over several images taken from the Mammogram Image Analysis Society (MIAS) image database. The MIAS offers a regular classification for mammographic studies. The system works faster so that any radiologist can take a clear decision about the appearance of calcifications by visual inspection.

Keywords: Breast Cancer; Morphologic Spectrum; Border Signature; Pixel Interpolation

Bibliography

  1. Jatoi Ismail and Manfred Kaufmann. "Management of breast diseases”. Berlin Heidelberg New York, Springer (2010).
  2. El Saghir., et al. “Trends in epidemiology and management of breast cancer in developing Arab countries: a literature and registry analysis". International Journal of Surgery4 (2007): 225-233.
  3. Shahin O R and Attiya G. “Classification of mammograms tumors using fourier analysis”. IJCSNS 2 (2014): 110.‏
  4. Shahin O., et al. “Breast cancer detection based on dynamic template matching”. Wulfenia Journal 12 (2013): 193-205.‏
  5. Shahin O R., et al. “Breast cancer detection using modified Hough transform”. Biomed Research 29 (2018): 3188-3191.
  6. Tahmoush David Alan. "Similarity Classification and Retrieval in Cancer Images and Informatics". University of Maryland, College Park in partial fulfillment of the requirements for the degree of Doctor of Philosophy (2008).
  7. Jirari Mohammed. "Computer Aided System For Detecting Masses In Mammograms". Kent State University in partial fulfilment of the requirements for the degree of Doctor of Philosophy (2008).
  8. Masotti Matteo, "Optimal image representations for mass detection in digital mammography". PhD diss. (2005).
  9. Shahin O R., et al. “Evolutionary Algorithm for Classification of Mass Lesions and Calcifications in Mammograms Using Fourier Analysis” (2012).
  10. Shahin O R., et al. “A Novel CAD System for Breast Cancer Detection”. Cancer Biology3 (2014): 335-340.‏
  11. Bhattacharya M., et al. “A study on genetic algorithm based hybrid softcomputing model for benignancy/malignancy detection of masses using digital mammogram”. International Journal of Computational Intelligence and Applications2 (2011): 141-165.‏
  12. Huang SF., et al. “Characterization of speculation on ultrasound lesions". IEEE Transactions on Medical Imaging 1 (2011): 111-121, 2011.
  13. Rangayyan M., et al. “A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs”. Journal of the Franklin Institute3 (2007): 312-348.
  14. Varela C., et al. “Use of border information in the classification of mammographic masses". Physics in Medicine and Biology 2 (2006): 425-441.
  15. Guliato D., et al. “Spiculation-Preserving Polygonal Modeling of Contours of Breast Tumors". In Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), New York (2006): 2791-2794.
  16. Rangayyan RM., et al. “Measures of Acutance and Shape for Classification of Breast Tumors". IEEE Transactions on Medical Imaging6 (1997): 799-810.
  17. Zhang Bao-Ning., et al. “Guidelines on the diagnosis and treatment of breast cancer". Gland Surgery1 (2012): 39-61.
  18. Al-Shamlan Hala and Ali El-Zaart. "Feature extraction values for breast cancer mammography images". In Bioinformatics and Biomedical Technology (ICBBT), International Conference (2010): 335-340.
  19. Shahin O R. “Brain tumor detection using watershed transform”. Annals of Clinical Cytology and Pathology 1 (2018): 1-6.‏
  20. Shahin O R., et al. “Breast Mass Detection in Mammograms using Modified K-means Clustering”. (2012).‏
  21. LM Bruce and R R Adhami. "Classifying mammographic mass shapes using the wavelet transform modulus-maxima method". IEEE Transactions on Medical Imaging 12 (1999): 1170-1177.
  22. S C Chen., et al. “Analysis of sonographic features for the differentiation of benign and malignant breast tumors of different sizes". Ultrasound in Obstetrics and Gynecology2 (2004): 188-193.
  23. Garra Brian S., et al. “Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis". Ultrasonic Imaging 4 (1993): 267-285.
  24. Stewart James. "Essential Calculus: Early Transcendentals". Cengage Learning (2012).
  25. Bourke Paul. "Calculating the area and centroid of a polygon" (1988).

Citation

Citation: Osama Rezq Shahin., et al. “Breast Cancer Classification Using: Pixel Interpolation". Acta Scientific Computer Sciences 3.11 (2021): 36-43.

Copyright

Copyright: © 2021 Osama Rezq Shahin., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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