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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, an 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.

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