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Multimedia Tools and Applications

, Volume 77, Issue 23, pp 31347–31362 | Cite as

A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing

  • Soukaina El Idrissi El Kaitouni
  • Abdelghafour Abbad
  • Hamid Tairi
Article
  • 253 Downloads

Abstract

In this article, we propose an automatic method for the detection and segmentation of the tumor on mammogram images. Most methods of detection of a tumor require an extraction of a large number of texture features from multiple calculations. The study first examines a technique of pre-processing images to obtain the Otsu thresholding method which eliminate items that do not belong in. After performing the thresholding, we estimate the number of base classes of technical LBP (Local Binary Pattern). To automate the initialization task, the classification proposed by applying dynamic k-means and improve the classes obtained by the method of Markov. Then we calculate the correlation between these classes and the original image, we deduce the class that contains the tumor and pectoral muscle. Finally, it uses the method of growing the region to eliminate pectoral muscle. The result obtained by this approach shows the quality and accuracy of extracting parts of the tumor compared to existing approaches in the literature.

Keywords

Classification Tumors Mammogram image Otsu thresholding LBP (Local Binary Pattern) K-means Markov 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Soukaina El Idrissi El Kaitouni
    • 1
  • Abdelghafour Abbad
    • 1
  • Hamid Tairi
    • 1
  1. 1.LIIAN, Department of Computer Science, Faculty of Sciences Dhar El MahrazUniversity Sidi Mohamed Ben AbdelahFezMorocco

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