Abstract
Recent developments in computer vision have enabled the availability of segmented images across various domains, such as medicine, where segmented radiography images play an important role in diagnosis-making. As prediction problems are common in medical image analysis, this work explores the use of segmented images (through the associated contours they highlight) as predictors in a supervised classification context. Consequently, we develop a new approach for image analysis that takes into account the shape of objects within images. For this aim, we introduce a new formalism that extends the study of single random planar curves to the joint analysis of multiple planar curves-referred to here as multivariate planar curves. In this framework, we propose a solution to the alignment issue in statistical shape analysis. The obtained multivariate shape variables are then used in functional classification methods through tangent projections. Detection of cardiomegaly in segmented X-rays and numerical experiments on synthetic data demonstrate the appeal and robustness of the proposed method.
Abstract (translated)
近期在计算机视觉领域的进展使得各种领域（如医学）中的分割图像变得可用，其中放射影像的分割图在诊断中发挥重要作用。鉴于预测问题是医学影像分析中的常见问题，本研究探索了将分割图像（通过它们所强调的轮廓）作为监督分类上下文中预测因子的应用。因此，我们开发了一种新的图像分析方法，该方法考虑到了图像内对象的形状。为了实现这一目标，我们引入了一种新形式化的方法，它扩展了对单一随机平面曲线的研究到多个平面曲线（在本文中称为多元平面曲线）的同时分析。在此框架下，我们提出了解决统计形状分析中的对齐问题的一种方案。所获得的多元形状变量随后被用于通过切线投影应用于功能性分类方法。心脏病理性心脏扩大在分割X光片上的检测以及合成数据的数值实验展示了所提议的方法的魅力和鲁棒性。
URL
https://arxiv.org/abs/2508.11780
