Evaluation of Terahertz Imaging for Breast Cancer Detection using Image Morphing

This thesis proposes the use of a mesh morphing algorithm for the quantitative evaluation of terahertz (THz) images. This work differs from traditional evaluation methods based on qualitative evaluation because it provides a fair and quantitative measurement of the THz imaging system\u27s performance. The objective of the algorithm is to match the alignment, shape, and resolution of the THz and reference pathology images. Therefore, the proposed morphing method provides a pathology reference for a pixel-by-pixel evaluation of the region classification in the THz image. To achieve this, the morphing algorithm aligns the images using the Pearson\u27s correlation coefficient and reshapes the pathology results using mesh morphing and homography estimation. The results presented in this thesis demonstrate the potential of the algorithm as an evaluation method of THz imaging for breast cancer detection in both fresh and formalin-fixed, paraffin-embedded (FFPE) murine tumors

Statistical Machine Learning for Breast Cancer Detection with Terahertz Imaging

Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue in BCS samples. In particular, this dissertation covers the description of four segmentation algorithms for the detection of breast cancer in THz imaging. We first explore the performance of one-dimensional (1D) Gaussian mixture and t-mixture models with Markov chain Monte Carlo (MCMC). Second, we propose a novel low-dimension ordered orthogonal projection (LOOP) algorithm for the dimension reduction of the THz information through a modified Gram-Schmidt process. Once the key features within the THz waveform have been detected by LOOP, the segmentation algorithm employs a multivariate Gaussian mixture model with MCMC and expectation maximization (EM). Third, we explore the spatial information of each pixel within the THz image through a Markov random field (MRF) approach. Finally, we introduce a supervised multinomial probit regression algorithm with polynomial and kernel data representations. For evaluation purposes, this study makes use of fresh and formalin-fixed paraffin-embedded (FFPE) heterogeneous human and mice tissue models for the quantitative assessment of the segmentation performance in terms of receiver operating characteristics (ROC) curves. Overall, the experimental results demonstrate that the proposed approaches represent a promising technique for tissue segmentation within THz images of freshly excised breast cancer samples