Abstract
Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. However, it is hard to keep markers attached on a body surface for weeks, and rather difficult to detect anatomic fiducial markers and match them in the infrared image during registration process. The proposed study, automatic longitudinal infrared registration algorithm, develops an automatic vascular intersection detection method and establishes feature descriptors by shape context to achieve robust matching, as well as to obtain control points for the deformation model. In addition, competitive winner-guided mechanism is developed for optimal corresponding. The proposed algorithm is evaluated in two ways. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels. These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images.
Highlights
Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step
Longitudinal IR image registration is an essential step toward the quantitative pixel-wise analysis of the heat energy and pattern change in a time course study
In longitudinal medical image registration, structural change of tumors or blood vessels can be seen on Magnetic Resonance Imaging (MRI) scans, which is why the over the past decade, topics of longitudinal medical image registration have mostly been associated with MRI brain scanning
Summary
Long-term comparisons of infrared image can facilitate the assessment of breast cancer tissue growth and early tumor detection, in which longitudinal infrared image registration is a necessary step. Results show that the algorithm can quickly lead to accurate image registration and that the effectiveness is superior to manual registration with a mean error being 0.91 pixels These findings demonstrate that the proposed registration algorithm is reasonably accurate and provide a novel method of extracting a greater amount of useful data from infrared images. Sequential images with temporal information, i.e., longitudinal images, can reduce individual variation and characterize more accurately anatomic and functional changes in tumor growth to respond to chemotherapy monitoring or breast cancer detection by computing the IR energy of heat patterns. Because longitudinal images can reduce individual variation and detect more accurately functional or structural changes in sequential images with temporal information, it has been increasingly applied and focused clinically[7]
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