research-article

Otsu’s thresholding technique for MRI image brain tumor segmentation

Authors:

F. Mebarek-Oudina,

Nadeem A. KhanAuthors Info & Claims

Multimedia Tools and Applications, Volume 81, Issue 30

Pages 43837 - 43849

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

Published: 01 December 2022 Publication History

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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Cited By

Farnoosh RAghagoli F(2024)Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clusteringNeurocomputing10.1016/j.neucom.2024.127551583:COnline publication date: 28-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127551
Ahmad SOgala JIkpotokin FArif MAhmad JMehfuz S(2024)Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over CloudSN Computer Science10.1007/s42979-024-02756-x5:4Online publication date: 6-Apr-2024
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Maalej ZBen Rejab FNouira K(2024)Comparative Study of Segmentation Techniques for Basal Ganglia Detection Based on Positron Emission Tomography ImagesSN Computer Science10.1007/s42979-024-02677-95:4Online publication date: 29-Mar-2024
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Index Terms

Otsu’s thresholding technique for MRI image brain tumor segmentation
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Video segmentation
  2. Machine learning
    1. Machine learning algorithms

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 81, Issue 30

Dec 2022

1561 pages

ISSN:1380-7501

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2022

Accepted: 11 May 2022

Revision received: 17 February 2022

Received: 02 March 2021

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Cited By

Farnoosh RAghagoli F(2024)Brain magnetic resonance images segmentation via improved mixtures of factor analyzers based on dynamic co-clusteringNeurocomputing10.1016/j.neucom.2024.127551583:COnline publication date: 28-May-2024
https://dl.acm.org/doi/10.1016/j.neucom.2024.127551
Ahmad SOgala JIkpotokin FArif MAhmad JMehfuz S(2024)Enhanced CNN-DCT Steganography: Deep Learning-Based Image Steganography Over CloudSN Computer Science10.1007/s42979-024-02756-x5:4Online publication date: 6-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02756-x
Maalej ZBen Rejab FNouira K(2024)Comparative Study of Segmentation Techniques for Basal Ganglia Detection Based on Positron Emission Tomography ImagesSN Computer Science10.1007/s42979-024-02677-95:4Online publication date: 29-Mar-2024
https://dl.acm.org/doi/10.1007/s42979-024-02677-9
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Zhang Q(2024)Big health data for elderly employees job performance of SOEs: visionary and enticing challengesMultimedia Tools and Applications10.1007/s11042-023-15355-483:2(4409-4442)Online publication date: 1-Jan-2024
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Alsenani TAyon SYousuf SAnik FChowdhury M(2023)Intelligent feature selection model based on particle swarm optimization to detect phishing websitesMultimedia Tools and Applications10.1007/s11042-023-15399-682:29(44943-44975)Online publication date: 1-Dec-2023
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