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Local Binary Pattern-Based Texture Analysis to Predict IDH Genotypes of Glioma Cancer Using Supervised Machine Learning Classifiers

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Emerging Technologies in Data Mining and Information Security

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1300))

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Abstract

Nowadays, machine learning-based quantified assessment of glioma has recently gained more attention by researchers in the field of medical image analysis. Such analysis makes use of either hand-crafted radiographic features with radiomic-based methods or auto-extracted features using deep learning-based methods. Radiomic-based methods cover a wide spectrum of radiographic features including texture, shape, volume, intensity, histogram, etc. The objective of the paper is to demonstrate the discriminative role of textures for molecular categorization of glioma using supervised machine learning techniques. This work aims to make state-of-the-art machine learning solutions available for magnetic resonance imaging (MRI)-based genomic analysis of glioma as a simple and sufficient technique based on single feature type, i.e., textures. The potential of this work demonstrates importance of texture features using simple, computationally efficient local binary pattern (LBP) method for isocitrate dehydrogenase (IDH)-based discrimination of glioma as IDH mutant and IDH wild type. Further, such texture-based discriminative analysis alone can definitely facilitate an immediate recommendation for further diagnostic decisions and personalized treatment plans for glioma patients.

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Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, India

    Sonal Gore & Jayant Jagtap

  2. Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India

    Sonal Gore

Authors
  1. Sonal Gore
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  2. Jayant Jagtap
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Corresponding author

Correspondence to Jayant Jagtap .

Editor information

Editors and Affiliations

  1. Faculty of Computers And Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. CHRIST (Deemed to be University), Bengaluru, Karnataka, India

    Siddhartha Bhattacharyya

  3. Institute of Engineering & Management, Kolkata, West Bengal, India

    Satyajit Chakrabati

  4. Institute of Engineering & Management, Kolkata, West Bengal, India

    Abhishek Bhattacharya

  5. Institute of Engineering & Management, Kolkata, West Bengal, India

    Soumi Dutta

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Gore, S., Jagtap, J. (2021). Local Binary Pattern-Based Texture Analysis to Predict IDH Genotypes of Glioma Cancer Using Supervised Machine Learning Classifiers. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_1

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