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Lung Lesion Identification Using Geometrical Feature and Optical Flow Method from Computed Tomography Scan Images

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Intelligent Multimedia Signal Processing for Smart Ecosystems

Abstract

Lung lesion identification is essential to an early lung cancer diagnosis. Detecting lung cancer early may aid physicians in treating patients. This chapter presents a geometric feature and optical flow technique for diagnosing lung lesions using computed tomography images. According to prior research, automating lung segmentation is incredibly challenging since fluctuations in pulmonary inflation combined with an elastic chest wall can result in a great deal of volume and margin variability. In addition, the attributes used to describe a lung lesion emphasize image aspects such as geometry, appearance, texture, and others. In this study, lung lesions in computed tomography images are segmented using an image processing technique that uses image segmentation algorithms. The optical flow approach has been designed to work with various computed tomography scan slices that could contain lesions. Collected data, image segmentation, optical flow, and performance evaluation are among the stages of the recommended method that call for image processing techniques. The Advanced Medical and Dental Institute, Universiti Sains Malaysia database was used to gather the computed tomography scan images. According to the study, lung slices with lesions have a standard deviation of 0% and 2.0% for the optical flow method, while slices without lesions have a standard deviation between 2.1% and 9.2%. These results can aid radiologists in more accurately diagnosing lung cancer by helping them immediately identify slices with lesions.

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Acknowledgement

This research work was financially supported by the Ministry of Higher Education Grant Scheme (FRGS) “A new Motion-based Optical flow Cancer Detection method for Lung Nodule Segmentation on Lung CT-Scan Images” (Ref: FRGS/1/2021/TK0/UITM/02/58) and ethics from Universiti Sains Malaysia (USM/JEPeM/19040231). The authors would like to express their gratitude to the Machine Learning Research Group (MLRG) members, Integrative Pharmacogenomics Institute (iPROMISE), and Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang for their assistance and guidance during the fieldwork. Finally, the authors thank Universiti Teknologi MARA, Cawangan Pulau Pinang, for their immense administrative and financial support.

Author information

Authors and Affiliations

  1. Centre for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh, Penang, Malaysia

    Mohd Firdaus Abdullah, Muhammad Khusairi Osman, Samsul Setumin & Adi Izhar Che Ani

  2. Integrative Pharmacogenomics Institute (iPROMISE), UiTM Puncak Alam Campus, Puncak Alam, Selangor, Malaysia

    Siti Noraini Sulaiman

  3. Department of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

  4. Breast Cancer Translational Research Programme (BCTRP), Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas, Pulau Pinang, Malaysia

    Noor Khairiah A. Karim

  5. Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Penang, Malaysia

    Siti Noraini Sulaiman

  6. Advanced Rehabilitation Engineering in Diagnostic and Monitoring Research Group (AREDiM), Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Penang, Malaysia

    Siti Noraini Sulaiman

Authors
  1. Mohd Firdaus Abdullah
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  2. Siti Noraini Sulaiman
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  3. Muhammad Khusairi Osman
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  4. Noor Khairiah A. Karim
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  5. Samsul Setumin
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  6. Adi Izhar Che Ani
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Corresponding author

Correspondence to Muhammad Khusairi Osman .

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Editors and Affiliations

  1. Department of Electronics and Instrumentation, University of Kashmir, Srinagar, Jammu and Kashmir, India

    Shabir A. Parah

  2. Aligarh Muslim University, Aligarh, India

    Nasir N. Hurrah

  3. Department of Electronics Engineering, Aligarh Muslim University, AMU Aligarh, India

    Ekram Khan

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Abdullah, M.F., Sulaiman, S.N., Osman, M.K., Karim, N.K.A., Setumin, S., Ani, A.I.C. (2023). Lung Lesion Identification Using Geometrical Feature and Optical Flow Method from Computed Tomography Scan Images. In: Parah, S.A., Hurrah, N.N., Khan, E. (eds) Intelligent Multimedia Signal Processing for Smart Ecosystems. Springer, Cham. https://doi.org/10.1007/978-3-031-34873-0_7

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  • DOI: https://doi.org/10.1007/978-3-031-34873-0_7

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