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Brain Tumor Detection Using Manifold Ranking in FLAIR MRI

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Proceedings of ICETIT 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 605))

Abstract

The unstructured and amorphous shape and size of brain tumor makes the task of radiologists more complex and time-consuming while identifying tumorous tissues in magnetic resonance imaging (MRI) images. This paper proposes an automated method for brain tumor detection by exploiting the features of Fluid-Attenuated Inversion Recovery (flair) MRI in association with the graph-based manifold ranking algorithm. The proposed method is comprised of three major phases. In the first phase, Simple Linear Iterative Clustering (SLIC) superpixel method is used to convert the homogeneous pixels in the form of superpixels which in turn represent graphically in the 2-d axial brain flair MRI image. Rank of each superpixel or node is computed based on the affinity against certain selected nodes as the background prior in the second phase. The relevance of each node with the background prior is then computed and represented in the form of tumor map. The adaptive thresholding method is used to detect the whole tumor region from the obtained tumor map in the third phase. The method is implemented on numerous axial slices of the flair MRI modality from MICCAI brain tumor segmentation (BRATS) challenge 2012 and 2015 training datasets. Performance is evaluated in the form of the Dice Similarity Coefficient (DSC), Sensitivity and Positive Predictive Value (PPV). Experimental results obtained on various subjects of the benchmark dataset illustrate that the method detects the brain tumor region in accurate and efficient manner.

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Notes

  1. 1.

    https://www.smir.ch/BRATS/Start2012.

  2. 2.

    https://www.smir.ch/BRATS/Start2015.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Uttarakhand, Srinagar Garhwal, Uttarakhand, India

    Shiv Naresh Shivhare & Nitin Kumar

Authors
  1. Shiv Naresh Shivhare
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  2. Nitin Kumar
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Corresponding author

Correspondence to Shiv Naresh Shivhare .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

  2. Department of Electrical Engineering, IIT Delhi, Delhi, Delhi, India

    Bijaya Ketan Panigrahi

  3. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Nagender Kumar Suryadevara

  4. Institute of Information Technology and Management, Delhi, Delhi, India

    Sudhir Kumar Sharma

  5. USICT, GGSIPU, Delhi, Delhi, India

    Amit Prakash Singh

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Shivhare, S.N., Kumar, N. (2020). Brain Tumor Detection Using Manifold Ranking in FLAIR MRI. In: Singh, P., Panigrahi, B., Suryadevara, N., Sharma, S., Singh, A. (eds) Proceedings of ICETIT 2019. Lecture Notes in Electrical Engineering, vol 605. Springer, Cham. https://doi.org/10.1007/978-3-030-30577-2_25

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