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.

中文翻译：

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太赫兹成像对切除的乳腺癌肿瘤的图像变形评估。

本文提出了一种图像变形算法，用于对切除的乳腺癌肿瘤的太赫兹（THz）图像进行定量评估的方法。目前，有关太赫兹成像评估的大多数研究都依赖于定性评估，并且尚无确定的基准或程序来量化太赫兹成像性能。提出的变形算法提供了一种工具，可以将THz图像与组织病理学图像定量对齐。使用脉冲THz成像和光谱系统以反射模式对新鲜切除的异种移植鼠乳腺癌肿瘤进行成像。将肿瘤组织固定在福尔马林中并包埋在石蜡中后，会产生福尔马林固定的石蜡包埋（FFPE）组织块。为病理图像准备一块薄片，而直接从该块产生另一个太赫兹反射图像。我们开发了一种使用组织病理学图像的单应性映射来调整网格变形的算法，以调整对齐方式，形状和分辨率，以匹配THz图像中组织的外部轮廓。与依赖于源图像和目标图像的内部特征的常规图像变形算法不同，仅使用组织的外部轮廓来避免偏差。将无监督贝叶斯学习算法应用于THz图像，以对异种移植乳腺肿瘤中存在的癌症，脂肪和肌肉的组织区域进行分类。结果表明，与现有算法相比，提出的网格变形算法可以提供更有效，更准确的太赫兹成像评估。结果还显示，尽管FFPE组织的THz图像与病理图像高度吻合，