An image-processing method for detection of irregularities in tissue based on the measured auto-fluorescence signal of endogenous fluorophores, preferably lipofuscin, comprising the following steps: - supplying the method with measured auto-fluorescence of an eye retinal tissue in a form of (x,y,λ) datacube, where the spectral information is acquired with a wavelength step of maximally 30 nm and where the sum of the signal of all the acquired wavelengths at each image point represents the standard autofluorescence image intensity; - transforming the (x,y,λ) datacube into a 2D array of spectral density functions S ( λ )(x,y), a spectral density function being a function of wavelength A at each location (x,y) across the image of the retinal tissue; - approximating the spectral density function S ( λ )(x,y) at each pixel of the image by a sum of spectral components; - representing each spectral component by a unimodal function; - assigning each spectral component to either an emitter or an absorber; - defining the sign of each spectral component as positive for each emitter and as negative for each absorber; - associating at least one of the positively signed spectral components to an emitter such as lipofuscin; - associating at least one of the negatively signed spectral components to haemoglobin and defining its relative proportion with at least one parameter; - fitting the spectral density function S ( λ )(x,y) at each pixel of the image using the sum of all of the spectral components to derive the relative proportions of the spectral components and other used parameters at each pixel of the image; - obtaining spatial information of parameters of the spectral components, mainly peak positions and relative proportions, so that an accurate map of emitters and absorbers, for example haemoglobins, in the retina or close to the retinal pigment epithelium is generated. - supplying the method with measured auto-fluorescence of an eye retinal tissue in a form of (x,y,λ) datacube, where the spectral information is acquired with a wavelength step of maximally 30 nm and where the sum of the signal of all the acquired wavelengths at each image point represents the standard autofluorescence image intensity; - transforming the (x,y,λ) datacube into a 2D array of spectral density functions S ( λ )(x,y), a spectral density function being a function of wavelength A at each location (x,y) across the image of the retinal tissue; - approximating the spectral density function S ( λ )(x,y) at each pixel of the image by a sum of spectral components; - representing each spectral component by a unimodal function; - assigning each spectral component to either an emitter or an absorber; - defining the sign of each spectral component as positive for each emitter and as negative for each absorber; - associating at least one of the positively signed spectral components to an emitter such as lipofuscin; - associating at least one of the negatively signed spectral components to haemoglobin and defining its relative proportion with at least one parameter; - fitting the spectral density function S ( λ )(x,y) at each pixel of the image using the sum of all of the spectral components to derive the relative proportions of the spectral components and other used parameters at each pixel of the image; - obtaining spatial information of parameters of the spectral components, mainly peak positions and relative proportions, so that an accurate map of emitters and absorbers, for example haemoglobins, in the retina or close to the retinal pigment epithelium is generated. <EOS>