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Levels Propagation Approach to Image Segmentation: Application to Breast MR Images

  • Fatah BouchebbahEmail author
  • Hachem Slimani
Article
  • 69 Downloads

Abstract

Accurate segmentation of a breast tumor region is fundamental for treatment. Magnetic resonance imaging (MRI) is a widely used diagnostic tool. In this paper, a new semi-automatic segmentation approach for MRI breast tumor segmentation called Levels Propagation Approach (LPA) is introduced. The introduced segmentation approach takes inspiration from tumor propagation and relies on a finite set of nested and non-overlapped levels. LPA has several features: it is highly suitable to parallelization and offers a simple and dynamic possibility to automate the threshold selection. Furthermore, it allows stopping of the segmentation at any desired limit. Particularly, it allows to avoid to reach the breast skin-line region which is known as a significant issue that reduces the precision and the effectiveness of the breast tumor segmentation. The proposed approach have been tested on two clinical datasets, namely RIDER breast tumor dataset and CMH-LIMED breast tumor dataset. The experimental evaluations have shown that LPA has produced competitive results to some state-of-the-art methods and has acceptable computation complexity.

Keywords

Levels Propagation Image segmentation Breast MRI Tumor 

Notes

Acknowledgements

The authors are thankful to the anonymous referees for their valuable suggestions and comments which have helped in improving the quality of the paper and its presentation. Furthermore, we would like to thank the hospital staff of Chahids Mahmoudi Hospital, Tizi Ouzou, Algeria, who generously allowed us to collect real MRI breast images from the radiology service of their establishment. Most particularly, we are grateful to Dr Farid Kechih for his inestimable help, especially in anonymazing the medical images, making and validating the associated ground truth of the constructed dataset (CMH-LIMED). We also thank Mr Hamid Slimani, Mr Youcef Hannou, and Dr Mohamed Rabia for facilitating the contact with the hospital staff.

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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  1. 1.LIMED Laboratory, Computer Science DepartmentUniversity of BejaiaBejaiaAlgeria

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