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Assessment of Terahertz Imaging for Excised Breast Cancer Tumors with Image Morphing

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Abstract

This paper presents an image morphing algorithm for quantitative evaluation methodology of terahertz (THz) images of excised breast cancer tumors. Most current studies on the assessment of THz imaging rely on qualitative evaluation, and there is no established benchmark or procedure to quantify the THz imaging performance. The proposed morphing algorithm provides a tool to quantitatively align the THz image with the histopathology image. Freshly excised xenograft murine breast cancer tumors are imaged using the pulsed THz imaging and spectroscopy system in the reflection mode. Upon fixing the tumor tissue in formalin and embedding in paraffin, a formalin-fixed paraffin-embedded (FFPE) tissue block is produced. A thin slice of the block is prepared for the pathology image while another THz reflection image is produced directly from the block. We developed an algorithm of mesh morphing using homography mapping of the histopathology image to adjust the alignment, shape, and resolution to match the external contour of the tissue in the THz image. Unlike conventional image morphing algorithms that rely on internal features of the source and target images, only the external contour of the tissue is used to avoid bias. Unsupervised Bayesian learning algorithm is applied to THz images to classify the tissue regions of cancer, fat, and muscles present in xenograft breast tumors. The results demonstrate that the proposed mesh morphing algorithm can provide more effective and accurate evaluation of THz imaging compared with existing algorithms. The results also showed that while THz images of FFPE tissue are highly in agreement with pathology images, challenges remain in assessing THz imaging of fresh tissue.

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Acknowledgements

The authors would like to thank Dr. Rajaram and his students for handling the mice and providing the tumor tissues [11] used in this work.

Funding

This work was funded by the National Institutes of Health under Award No. R15CA208798. It was also funded in part by the National Science Foundation under Award Nos. 1408007 and 1711087.

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Authors and Affiliations

  1. Department of Electrical Engineering, University of Arkansas, Fayetteville, AR, 72701, USA

    Tanny Chavez, Tyler Bowman, Jingxian Wu & Magda El-Shenawee

  2. Oklahoma Animal Disease Diagnostic Laboratory, Oklahoma State University, Stillwater, OK, 74076, USA

    Keith Bailey

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  1. Tanny Chavez
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  2. Tyler Bowman
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  3. Jingxian Wu
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  4. Keith Bailey
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  5. Magda El-Shenawee
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Correspondence to Tanny Chavez.

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Chavez, T., Bowman, T., Wu, J. et al. Assessment of Terahertz Imaging for Excised Breast Cancer Tumors with Image Morphing. J Infrared Milli Terahz Waves 39, 1283–1302 (2018). https://doi.org/10.1007/s10762-018-0529-8

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  • DOI: https://doi.org/10.1007/s10762-018-0529-8

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