## 4 Results and Discussion
### Evaluation of Detection Results
The proposed segmentation methodology proves robust and accurate in identifying geometry and dimensions of brain tumors and margins in different tissue slice depths from the captured fluorescence images, emphasizing the potential of the proposed fluorescent tumor visualization and identification framework. Looking at Figures 6-10 it could be pointed out that the proposed approach tends to over- or under-estimate the region of interest (ROI). The overall conclusion extracted through the testing procedure is that the effect of mis-segmentation of ROI mainly depends on the structure of tissue and the spread/geometry of the tumor over it. Under-segmentation occurs for the complex shape of the overall tumor region border, characterized by irregular contour curves that morphological operators cannot follow. On the contrary, over-segmentation is observed when the borders between the tumor region (upper segment of the image) and normal tissue (lower part of the image) are unclear, meaning that increased intensity values are contributed by the upper limit of the normal tissue region. Due to the fact that no prior knowledge of image content is requested, the structuring element utilized for the application of mathematical morphology remains fixed in the proposed framework (we focus on the totally unsupervised pipeline) and does not totally adjust to the tumor margins of each image sample. Future improvements to the proposed segmentation pipeline could investigate an adaptive calculation procedure for determining optimal setup (size and shape of structuring element) of morphological operators to be utilized at the image refinement stage. Figure 6: Fluorescence images containing the brain tissue at 10 μm slice depth along with the ground truth tumor margins (red-colored boundary): (**a**) Original image, (**b**) processed image after applying Otsu thresholding to original image, (**c**) processed image after applying entropy-based thresholding to the original image, (**d**) processed image after performing mean shift clustering on the original image, (**e**) binary mask after fusion of the three segmentation results, and (**f**) ground truth (red) and estimated (blue) tumor margins superimposed on the original fluorescence image after the application of the proposed unsupervised learning segmentation scheme. Figure 7: Fluorescence images containing the brain tissue at 30 \(\upmu\)m slice depth along with the ground truth tumor margins (red-colored boundary): (**a**) Original image, (**b**) processed image after applying Otsu thresholding to the original image, (**c**) Processed image after applying entropy-based thresholding to the original image, (**d**) processed image after performing mean shift clustering on the original image, (**e**) binary mask after fusion of the 3 segmentation results and, (**f**) ground truth (red) and estimated (blue) tumor margins superimposed on the original fluorescence image after the application of the proposed unsupervised learning segmentation scheme. Figure 8: Fluorescence images containing the brain tissue in 60 μm slice depth along with the ground truth tumor margins (red-colored boundary): (**a**) Original image, (**b**) processed image after applying Otsu thresholding to original image, (**c**) Contour map of the processed image after performing entropy-based thresholding on the original image, (**d**) processed image after performing mean shift clustering on the original image, (**e**) binary mask after fusion of the 3 segmentation results, and (**f**) ground truth (red) and estimated (blue) tumor margins superimposed on the original fluorescence image after the application of the proposed unsupervised learning segmentation scheme.