通过鲁棒的累积分布匹配实现图像一致性，保留局部特征。
Image Harmonization using Robust Restricted CDF Matching
- 基于曲线拟合的非线性强度变换，保持图像平滑弹性。
- 与模板CDF匹配度高，有效降低输入数据差异性。
- 适用于MRI等多类影像数据，适合临床真实场景部署。
将机器学习算法应用于实际场景仍具挑战，主要源于输入数据的不可预测变异性，如用户、机构或扫描仪间的差异。通过稳健的数据预处理进行数据调和可缓解此问题。本文提出一种基于累积分布函数（CDF）匹配的图像调和方法，采用曲线拟合实现图像强度的非线性变换。该方法在保持局部变异性与个体重要特征的同时，实现平滑且弹性的转换，优于传统直方图匹配算法。非线性变换使输入图像与预定义模板的CDF达到高度匹配，同时弹性约束保障了局部细节的保留，有利于后续机器学习处理。相比基于机器学习的方法，该模板引导的策略更具直观可控性。尽管以MRI图像为例，但方法具有通用性，可推广至其他成像数据类型。
原文摘要 · Abstract (English)
Deployment of machine learning algorithms into real-world practice is still a difficult task. One of the challenges lies in the unpredictable variability of input data, which may differ significantly among individual users, institutions, scanners, etc. The input data variability can be decreased by using suitable data preprocessing with robust data harmonization. In this paper, we present a method of image harmonization using Cumulative Distribution Function (CDF) matching based on curve fitting. This approach does not ruin local variability and individual important features. The transformation of image intensities is non-linear but still ``smooth and elastic", as compared to other known histogram matching algorithms. Non-linear transformation allows for a very good match to the template. At the same time, elasticity constraints help to preserve local variability among individual inputs, which may encode important features for subsequent machine-learning processing. The pre-defined template CDF offers a better and more intuitive control for the input data transformation compared to other methods, especially ML-based ones. Even though we demonstrate our method for MRI images, the method is generic enough to apply to other types of imaging data.
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