Supporting dataset for Correction of mosaicking artifacts in multimodal images caused by uneven illumination

Supporting dataset for Chernavskaia O, et al. Correction of mosaicking artifacts in multimodal Images caused by uneven illumination. Journal of Chemometrics. 2017; 31:e2901. https://doi.org/10.1002/cem.2901 Recent advances in nonlinear multimodal imaging, eg, the combination of coherent anti-Stokes Raman scattering, 2-photon excited autofluorescence, and second-harmonic generation, have shown the great potential of this imaging technique for medical diagnostics. To extract reliable diagnostic information from these multimodal images, a complex image-processing pipeline is necessary. A major part of this image-processing pipeline is the elimination of the mosaicking artifact caused by an uneven illumination within the images. While this problem is well known in image processing of photographic images and methods to solve it were developed, their direct application to multimodal images does not yield satisfactory results. This fact results from the nonlinearity of the measurement modalities and characteristics of the multimodal images itself. In this contribution, different approaches to correct the mosaicking are considered and adapted to multimodal images. In this tutorial article, an investigation and comparative analysis of correction methods were performed, and practical recommendations for the application of different methods are given. The results of this paper can be applied to the development of complete or partial automatic software for medical diagnostics using nonlinear multimodal imaging techniques. The dataset contains all images and corrections provided in supporting information SI 1: original image, histogram adjusted method (HAM), boundaru adjustment method (BAM), Fourier method, and Fourier method combined with BAM.