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Radiomics Feature Selection from Thyroid Thermal Images to Improve Thyroid Nodules Interpretations

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Artificial Intelligence over Infrared Images for Medical Applications (AIIIMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14298))

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Abstract

The early detection of malignant nodules and timely diagnosis of thyroid abnormalities play a crucial role in improving medical treatment outcomes and minimizing disease progression. Thermography has emerged as an affordable and non-radiation method for detecting thyroid issues, reducing the risks associated with unnecessary invasive biopsies. By extracting radiomics features from thermal images of the thyroid, valuable information about the underlying tissue characteristics can be obtained, offering numerous advantages in the field of medical imaging. In this study, radiomics features were extracted from thermal images of the thyroid, and unsupervised feature selection techniques including Principal Component Analysis (PCA), Independent Component Analysis (ICA), and variance thresholding were employed to reduce the dimensionality of the feature set. It is important to acknowledge that the field of radiomics analysis in thermography thyroid images is still emerging, and further research is required to validate the clinical usefulness of these features. Nevertheless, radiomics analysis holds significant potential to enhance the assessment of thermography thyroid images and provide valuable insights into thyroid function and pathology.

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Acknowledgments

We would like to express our gratitude to Prof. Conci Aura and her team in Endocrinology and Surgery departments at the Hospital Universitário Antônio Pedro, Universidade Federal Fluminense, for providing us with the images.

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Authors and Affiliations

  1. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, 81745-33871, Isfahan, Iran

    Mahnaz Etehadtavakol & Mojtaba Sirati-Amsheh

  2. School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Eddie Y. K. Ng

Authors
  1. Mahnaz Etehadtavakol
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  2. Mojtaba Sirati-Amsheh
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  3. Eddie Y. K. Ng
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Corresponding author

Correspondence to Eddie Y. K. Ng .

Editor information

Editors and Affiliations

  1. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Siva Teja Kakileti

  2. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Geetha Manjunath

  3. Piedmont Physical Medicine and Rehabilitation, Greenville, SC, USA

    Robert G. Schwartz

  4. University of Manchester, Manchester, UK

    Alejandro F. Frangi

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Etehadtavakol, M., Sirati-Amsheh, M., Ng, E.Y.K. (2023). Radiomics Feature Selection from Thyroid Thermal Images to Improve Thyroid Nodules Interpretations. In: Kakileti, S.T., Manjunath, G., Schwartz, R.G., Frangi, A.F. (eds) Artificial Intelligence over Infrared Images for Medical Applications. AIIIMA 2023. Lecture Notes in Computer Science, vol 14298. Springer, Cham. https://doi.org/10.1007/978-3-031-44511-8_10

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  • DOI: https://doi.org/10.1007/978-3-031-44511-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45657-2

  • Online ISBN: 978-3-031-44511-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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