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Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Author

Listed:
  • Peter Chondro

    (Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Qazi Mazhar ul Haq

    (Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Shanq-Jang Ruan

    (Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan)

  • Lieber Po-Hung Li

    (Department of Otolaryngology, Cheng Hsin General Hospital, Taipei 112, Taiwan
    Faculty of Medicine, School of Medicine, National Yang-Ming University, Taipei 112, Taiwan
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan)

Abstract

Maxillary sinuses are the most prevalent locations for paranasal infections on both children and adults. Common diagnostic material for this particular disease is through the screening of occipitomental-view skull radiography (SXR). With the growing cases on paranasal infections, expediting the diagnosis has become an important innovation aspect that could be addressed through the development of a computer-aided diagnosis system. As the preliminary stage of the development, an automatic segmentation over the maxillary sinuses is required to be developed. This study presents a computer-aided detection (CAD) module that segments maxillary sinuses from a plain SXR that has been preprocessed through the novel texture-based morphological analysis (ToMA). Later, the network model from the Transferable Fully Convolutional Network (T-FCN) performs pixel-wise segmentation of the maxillary sinuses. T-FCN is designed to be trained with multiple learning stages, which enables re-utilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed system achieved segmentation accuracy at 85.70%, with 50% faster learning time.

Suggested Citation

  • Peter Chondro & Qazi Mazhar ul Haq & Shanq-Jang Ruan & Lieber Po-Hung Li, 2020. "Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs," Mathematics, MDPI, vol. 8(5), pages 1-15, May.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:5:p:768-:d:356674
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    References listed on IDEAS

    as
    1. Haidi Ibrahim & Seng Chun Hoo, 2014. "Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, February.
    2. Hafiz Syed Muhammad Muslim & Sajid Ali Khan & Shariq Hussain & Arif Jamal & Hafiz Syed Ahmed Qasim, 2019. "A knowledge-based image enhancement and denoising approach," Computational and Mathematical Organization Theory, Springer, vol. 25(2), pages 108-121, June.
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