Color Spaces in Data Fusion of Multi-temporal Images

The data fusion process is strongly recommended in biomedical applications. It allows a better detection and localization of the pathology, as well as the diagnosis and follow-up of many diseases [1], especially with multi-parametric or multi-temporal data. The independent visualization of multiple images from large volumes is a main cause of errors and inaccuracy within the interpretation process. In this respect, the use of color fusion methods allows to highlight small details from multi-temporal and multi-parametric images. In the present work, a color data fusion approach is proposed for multi-temporal images, in particular for images of the liver acquired through triphasic CT. The best color association has been studied considering various data sources. Different metrics for quality assessment have been selected from the color space theory, making an interesting comparison with the human visual perception.