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Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach

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Abstract

Cancer is the second leading cause of deaths worldwide, reported by World Health Organization (WHO). The abnormal growth of cells, which should die at the time but they remained in body organ which makes tumor and brain tumor is one of them. During its treatment planning, brain tumor segmentation plays its vital role, Magnetic Resonance Imaging (MRI) is most widely used medical imaging modalities to scan brain tissues, and segmentation of brain tumor from MRI scans is still a challenging task, due to the variability in spatial, structure and appearance of the brain tumor. The existing brain tumor segmentation techniques are still suffering from an inadequate performance, dependent on initial assumptions, and required manual interference. The main challenge is to segment out the accurate tumor from MRI images, and to give the solution for its variability in size due to spatial change in image slices. The proposed model in an automated manners segment out abnormal tissues from MRI images. The proposed model has some aspects like we apply some pre-processing techniques, and apply superpixel-segmentation with their improved tuned parameter values. We have extracted different features for the superpixels in the images such that statistical features, fractal features, texton features, curvature feature and SIFT features. Due to unbalanced feature vector, we have proposed class balancing algorithm, and then apply SVM, KNN, Decision Tree and Ensemble classifiers, to classify the normal and abnormal superpixels. To evaluate the proposed model, we used MICCAI BRATS-2017 MRI training dataset. The Dice Coefficient (DSC), precision, sensitivity, and balanced error rate (BER) against the ground truths for FLAIR sequence in LGG volumes have been obtained as 0.8593, 87%, 93%, and 0.08 respectively. The DSC, precision, sensitivity, and BER against the ground truths for FLAIR sequence in HGG volumes have been obtained as 0.8528, 87%, 97%, and 0.08 respectively. It is evident from the quantitative and visual results that the proposed model provides a close match to the expert delineation for the FLAIR sequence.

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Notes

  1. https://www.who.int/news-room/fact-sheets/detail/cancer.

  2. http://www0.cs.ucl.ac.uk/staff/ucacarr/teaching/ndsp/curvature.pdf.

  3. https://www.med.upenn.edu/sbia/brats2017/data.html.

  4. https://www.med.upenn.edu/sbia/brats2017/data.html.

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, The Superior University, Lahore, Pakistan

    Muhammad Javaid Iqbal

  2. Department of Computer Science, COMSATS University Islamabad, Lahore Campus. 1.5 KM Defence Road off Raiwind Road, Lahore, Pakistan

    Usama Ijaz Bajwa, Muhammad Aksam Iftikhar & Muhammad Waqas Anwar

  3. Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Ghulam Gilanie

Authors
  1. Muhammad Javaid Iqbal
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  2. Usama Ijaz Bajwa
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  3. Ghulam Gilanie
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  4. Muhammad Aksam Iftikhar
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  5. Muhammad Waqas Anwar
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Correspondence to Usama Ijaz Bajwa.

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Iqbal, M.J., Bajwa, U.I., Gilanie, G. et al. Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach. Multimed Tools Appl 81, 38409–38427 (2022). https://doi.org/10.1007/s11042-022-13166-7

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  • DOI: https://doi.org/10.1007/s11042-022-13166-7

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