A computer-implemented method of processing image data representing biological units in a tissue sample, the computer including a processor, the method comprising: receiving, by the processor, a first image of the tissue sample containing signals from an immunofluorescent morphological marker, wherein the tissue sample is stained with the IF morphological marker; receiving, by the processor, a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe; registering (130;712), by the processor, locations of signals from the IF morphological marker in the first image with locations of signals from the fluorescent probe in the second image to produce a registered image; segmenting (140;714), by the processor, the second image to identify nuclei in the tissue sample based on the locations of signals from the fluorescent probe in the second image, wherein segmenting the nuclei includes applying, by the processor, a wavelet transform to the second image; generating, by the processor, a Voronoi partition and rings generated from a threshold distance map from the nuclei in the second image in which each respective ring is constrained to at least partially surround only one nucleus in the tissue sample; classifying (150;706), by the processor, each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image; performing (160,170;708), by the processor, a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom; and filtering (180;710), by the processor, the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one of the at least two classes only. receiving, by the processor, a first image of the tissue sample containing signals from an immunofluorescent morphological marker, wherein the tissue sample is stained with the IF morphological marker; receiving, by the processor, a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe; registering (130;712), by the processor, locations of signals from the IF morphological marker in the first image with locations of signals from the fluorescent probe in the second image to produce a registered image; segmenting (140;714), by the processor, the second image to identify nuclei in the tissue sample based on the locations of signals from the fluorescent probe in the second image, wherein segmenting the nuclei includes applying, by the processor, a wavelet transform to the second image; generating, by the processor, a Voronoi partition and rings generated from a threshold distance map from the nuclei in the second image in which each respective ring is constrained to at least partially surround only one nucleus in the tissue sample; classifying (150;706), by the processor, each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image; performing (160,170;708), by the processor, a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom; and filtering (180;710), by the processor, the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one of the at least two classes only.