Lin Fan-Ya, Lee Chi-En, Chen Chung-Ming, Chang Yeun-Chung, Huang Chiun-Sheng
The Department of Biomedical Engineering, National Taiwan University, No. 1, Section 1, Jen-Ai Rd., Taipei 100, Taiwan.
The Department of Medical Image, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Changde Street, Zhongzheng District, Taipei City, 100, Taiwan.
Phys Med Biol. 2023 Dec 13;68(24). doi: 10.1088/1361-6560/ad0357.
The automated marker-free longitudinal Infrared (IR) breast image registration overcomes several challenges like no anatomic fiducial markers on the body surface, blurry boundaries, heat pattern variation by environmental and physiological factors, nonrigid deformation, etc, has the ability of quantitative pixel-wise analysis with the heat energy and patterns change in a time course study. To achieve the goal, scale-invariant feature transform, Harris corner, and Hessian matrix were employed to generate the feature points as anatomic fiducial markers, and hybrid genetic algorithm and particle swarm optimization minimizing the matching errors was used to find the appropriate corresponding pairs between the 1st IR image and theth IR image. Moreover, the mechanism of the IR spectrogram hardware system has a high level of reproducibility. The performance of the proposed longitudinal image registration system was evaluated by the simulated experiments and the clinical trial. In the simulated experiments, the mean difference of our system is 1.64 mm, which increases 57.58% accuracy than manual determination and makes a 17.4% improvement than the previous study. In the clinical trial, 80 patients were captured several times of IR breast images during chemotherapy. Most of them were well aligned in the spatiotemporal domain. In the few cases with evident heat pattern dissipation and spatial deviation, it still provided a reliable comparison of vascular variation. Therefore, the proposed system is accurate and robust, which could be considered as a reliable tool for longitudinal approaches to breast cancer diagnosis.
自动化无标记纵向红外（IR）乳腺图像配准克服了多个挑战，如体表无解剖基准标记、边界模糊、环境和生理因素导致的热模式变化、非刚性变形等，能够在时间进程研究中对热能和模式变化进行逐像素定量分析。为实现这一目标，采用尺度不变特征变换、哈里斯角点和黑森矩阵来生成作为解剖基准标记的特征点，并使用混合遗传算法和粒子群优化来最小化匹配误差，以找到第一幅红外图像与第(n)幅红外图像之间合适的对应对。此外，红外光谱硬件系统的机制具有高度的可重复性。通过模拟实验和临床试验对所提出的纵向图像配准系统的性能进行了评估。在模拟实验中，我们系统的平均差异为(1.64)毫米，比手动测定的准确率提高了(57.58%)，比先前的研究提高了(17.4%)。在临床试验中，(80)名患者在化疗期间多次采集红外乳腺图像。其中大多数在时空域中对齐良好。在少数热模式明显消散和空间偏差的情况下，它仍然能够提供血管变化的可靠比较。因此，所提出的系统准确且稳健，可被视为乳腺癌诊断纵向方法的可靠工具。
