A computer-implemented method for identifying at least one feature of a tissue sample in a microscopic image, the method comprising: - providing a first image of a fluorescence microscopy imaging modality representing an area of the tissue sample; - providing a second image of a dark field microscopy imaging modality representing the said area of the tissue sample; and, either - generating a first high intensity image by applying a first high intensity filter to identify pixels above a threshold in the first image to obtain first information of the at least one feature ; - generating a second low intensity image by applying a second low intensity filter to identify pixels below a threshold in the second image to obtain second information of the at least one feature; - calculating a correlation of the first high intensity image and the second low intensity image for correlating the first information and the second information of the at least one feature; - outputting the calculated correlation for identifying the at least one feature of the tissue sample; or - generating a first low intensity image by applying a first low intensity filter to identify pixels below a threshold in the first image to obtain the first information of the at least one feature ; - generating a second high intensity image by applying a second high intensity filter to identify pixels above a threshold in the second image to obtain the second information of the at least one feature; - calculating a correlation of the first low intensity image and the second high intensity image for correlating the first information and the second information of the at least one feature; - outputting the calculated correlation for identifying the at least one feature of the tissue sample. - providing a first image of a fluorescence microscopy imaging modality representing an area of the tissue sample; - providing a second image of a dark field microscopy imaging modality representing the said area of the tissue sample; and, either - generating a first high intensity image by applying a first high intensity filter to identify pixels above a threshold in the first image to obtain first information of the at least one feature ; - generating a second low intensity image by applying a second low intensity filter to identify pixels below a threshold in the second image to obtain second information of the at least one feature; - calculating a correlation of the first high intensity image and the second low intensity image for correlating the first information and the second information of the at least one feature; - outputting the calculated correlation for identifying the at least one feature of the tissue sample; or - generating a first low intensity image by applying a first low intensity filter to identify pixels below a threshold in the first image to obtain the first information of the at least one feature ; - generating a second high intensity image by applying a second high intensity filter to identify pixels above a threshold in the second image to obtain the second information of the at least one feature; - calculating a correlation of the first low intensity image and the second high intensity image for correlating the first information and the second information of the at least one feature; - outputting the calculated correlation for identifying the at least one feature of the tissue sample. <EOS>