Papers › Statistical Inference on Grayscale Images via the Euler-Radon Transform
Statistical Inference on Grayscale Images via the Euler-Radon Transform
Kun Meng, Mattie Ji, Jinyu Wang, Kexin Ding, Henry Kirveslahti, Ani Eloyan, Lorin Crawford
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Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where pixel intensities instead take on continuous values) have been relatively underdeveloped. In this paper, we introduce the Euler-Radon transform, which generalizes the Euler characteristic transform to grayscale images by using o-minimal structures and Euler integration over definable functions. Coupling the Karhunen-Loeve expansion with our proposed topological representation, we offer hypothesis-testing algorithms based on the chi-squared distribution for detecting significant differences between two groups of grayscale images. We illustrate our framework via extensive numerical experiments and simulations.
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