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Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix

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Medical Imaging in Clinical Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 651))

Abstract

The present chapter proposes an automatic segmentation method that performs multilevel image thresholding by using the spatial information encoded in the gray level co-occurrence matrix (GLCM). The 2D local cross entropy approach that has been designed by extending the one dimensional (1-D) cross entropy thresholding method to a two dimensional (2D) one using the GLCM, serves as a fitness function. The use of conventional exhaustive search based implementations for multilevel thresholding are computationally expensive. Under such conditions evolutionary algorithm like particle swarm optimization (PSO) has been used. The effectiveness of this method was tested on brain tumor MR images and comparison was done with seven other level set based segmentation algorithms, using three different measures (1) Jaccard, (2) Dice and (3) Root mean square error (RMSE). The results demonstrate that average metric values were equal to 0.881902, 0.936394 and 0.070123 for proposed approach, which were significantly better than existing techniques.

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Acknowledgments

The authors gratefully acknowledge the efforts by Dr. Sandeep Singh Pawar (Advance Diagnostic Centre, Ludhiana, Punjab) for providing the clinical interpretations.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Taranjit Kaur & Barjinder Singh Saini

  2. Department of Computer Science and Engineering, University Institute of Engineering and Technology (UIET), Panjab University, Chandigarh, India

    Savita Gupta

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  1. Taranjit Kaur
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  2. Barjinder Singh Saini
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  3. Savita Gupta
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Corresponding authors

Correspondence to Taranjit Kaur , Barjinder Singh Saini or Savita Gupta .

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Editors and Affiliations

  1. Techno India College of Technology, Durgapur, West Bengal, India

    Nilanjan Dey

  2. Professional Colleges (SRMGPC), Shri Ramswaroop Memorial Group of, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Faculty of Computers & Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Kaur, T., Saini, B.S., Gupta, S. (2016). Optimized Multi Threshold Brain Tumor Image Segmentation Using Two Dimensional Minimum Cross Entropy Based on Co-occurrence Matrix. In: Dey, N., Bhateja, V., Hassanien, A. (eds) Medical Imaging in Clinical Applications. Studies in Computational Intelligence, vol 651. Springer, Cham. https://doi.org/10.1007/978-3-319-33793-7_20

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  • DOI: https://doi.org/10.1007/978-3-319-33793-7_20

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