LISA：用于纵向图像序列分析的MATLAB软件包
LISA: a MATLAB package for Longitudinal Image Sequence Analysis
摘要：
现在可以以前所未有的规模获得大序列图像（或电影），这对现有的图像分析技术提出了根本性的挑战。挑战包括异质性、（自动）对齐、多重比较、潜在伪影和隐藏噪声。本文介绍了我们的MATLAB软件包，纵向图像序列分析（LISA），作为一个一站式的集成图像处理和分析工具，比较一般类的图像从不同的时间，会话，或主题。给定两个对比图像序列，LISA中的图像处理从在两个代表性图像中选择感兴趣区域开始，然后进行自动或手动分割和配准。自动分割使用像素强度值的混合高斯分布对图像进行去噪，而手动分割应用用户选择的强度截止值来滤除噪声。自动配准基于中线回归对齐对比图像，而手动配准沿着由两个用户选择的点形成的参考线对齐图像。然后，处理后的图像被渲染用于同时进行统计比较，以生成D、S、T和P图。D图表示对比图像的策划差异，S图是非参数化平滑差异，T图表示方差调整的平滑差异，P图提供多重性控制的p值。这些图显示了由于纵向、受试者特异性或治疗变化而具有显著差异的区域。如果图像已经被处理，则用户可以跳过图像处理步骤以直接进入统计分析步骤。因此，LISA提供了应用其他图像预处理工具的灵活性。LISA还为高清图像提供了并行计算选项。
英文摘要：
Large sequences of images (or movies) can now be obtained on an unprecedented scale, which poses fundamental challenges to the existing image analysis techniques. The challenges include heterogeneity, (automatic) alignment, multiple comparisons, potential artifacts, and hidden noises. This paper introduces our MATLAB package, Longitudinal Image Sequence Analysis (LISA), as a one-stop ensemble of image processing and analysis tool for comparing a general class of images from either different times, sessions, or subjects. Given two contrasting sequences of images, the image processing in LISA starts with selecting a region of interest in two representative images, followed by automatic or manual segmentation and registration. Automatic segmentation de-noises an image using a mixture of Gaussian distributions of the pixel intensity values, while manual segmentation applies a user-chosen intensity cut-off value to filter out noises. Automatic registration aligns the contrasting images based on a mid-line regression whereas manual registration lines up the images along a reference line formed by two user-selected points. The processed images are then rendered for simultaneous statistical comparisons to generate D, S, T, and P-maps. The D map represents a curated difference of contrasting images, the S map is the non-parametrically smoothed differences, the T map presents the variance-adjusted, smoothed differences, and the P-map provides multiplicity-controlled p-values. These maps reveal the regions with significant differences due to either longitudinal, subject-specific, or treatment changes. A user can skip the image processing step to dive directly into the statistical analysis step if the images have already been processed. Hence, LISA offers flexibility in applying other image pre-processing tools. LISA also has a parallel computing option for high definition images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
