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Otsu’s thresholding technique for MRI image brain tumor segmentation

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Abstract

MRI image segmentation is very challenging area in medical image processing. It is implemented with the low contract of MRI scan. In terms of certain input features or expert information, the major objective of medical image segmentation is to isolate and describe anatomical constitutions. In MRI image segmentation, brain tumor segmentation is more difficult because of its complex structure. The Otsu’s thresholding method is well-known method in image segmentation. In this paper, choosing the classes or bins of Otsu’s thresholding are analyzed on MRI image brain tumor segmentation. As a preprocessing, the 2D MRI images are convert the grayscale image and resized to the same size. And then, median filter is utilized to eliminate the noise from MRI image. In MRI image segmentation, the varieties of classes or bins of Otsu’s thresholding are utilized to segment the brain tumor from MRI images. Then, the morphological operation is used to achieve the accurate tumor regions. All of the experiments are tested on 2015 BRATS dataset. As segmentation quality validation metric, Jaccard similarity index, true positive rate (Sensitivity), true negative rate (Specificity) and accuracy are used to validate the segmented results and their ground truth. According to the results, level 4 or class 4 got 68.7955% in true positive and 95.5593% in accuracy. Class 4 is the best or suitable for MRI image segmentation according to experiments.

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Authors and Affiliations

  1. Faculty of Computer Science, University of Computer Studies, Pakokku, Myanmar

    Myat Thet Nyo

  2. Department of Physics, Faculty of Sciences, University of 20 août 1955-Skikda, Skikda, Algeria

    F. Mebarek-Oudina

  3. Faculty of Information Science, Myanmar Institute of Information Technology, Mandalay, Myanmar

    Su Su Hlaing

  4. Department of Civil Engineering, Jamia Millia Islamia, -110025, New Delhi, India

    Nadeem A. Khan

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  1. Myat Thet Nyo
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  2. F. Mebarek-Oudina
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  3. Su Su Hlaing
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  4. Nadeem A. Khan
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Correspondence to F. Mebarek-Oudina.

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Nyo, M.T., Mebarek-Oudina, F., Hlaing, S.S. et al. Otsu’s thresholding technique for MRI image brain tumor segmentation. Multimed Tools Appl 81, 43837–43849 (2022). https://doi.org/10.1007/s11042-022-13215-1

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  • DOI: https://doi.org/10.1007/s11042-022-13215-1

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