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This paper presents an IF1F2 color space for describing colors in microscopic images. Unlike the classical color spaces that treat the R, G, and B (RGB) components of a pixel’s color as independent elements, the proposed color space treats RGB as a sampled spectral signal, called a RGB signal. Then, based on the Fourier spectrum analysis of the RGB signal, the 1-D discrete Fourier transform is introduced to describe the color features of microscopic images. K-means clustering experiments on two microscopic image datasets validate the superiority of the proposed IF1F2 color space compared with the classical RGB and HSV (hue, saturation, and value) color spaces.

A novel RGB Fourier transform-based color space for optical microscopic image processing