In cancer research, there is a need to make accurate spatial measurements in multi-layered fluorescence microscopy images. Researchers would like to measure distances in and between biological objects such as nerves and tumours, to investigate questions which includes if nerve distribution in and around tumours can have a prognostic value in cancer diagnostics. This thesis is split into two parts, the first being: given arbitrary florescent images of cancer tissue samples, investigate the feasibility of automatically identifying nerves, tumours and blood vessels using classic image analysis. The second part is: given an image with identified objects, quantify their spatial data. By analysing 58 different cancer tissue samples we found that a modified Otsu method gives the most promising results for image segmentation. We found that non-verifiable objects and verifiable objects share the same pixel intensity distributions which implies that it is in general not possible to solely use thresholding methods to separate them from each other. For the spatial analysis, two measurement methods were introduced. An object based method that provides measurements from the edges of nerves to tumour edges, and a pixel based measurement method, which provides fraction based measurements that are comparable between different tissue samples.