## 4 Results and Discussion
### Evaluation of Detection Results
Figure 9: Fluorescence images containing the brain tissue in 100 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. At this stage, we can also emphasize the efficiency of the entropy thresholding procedure to derive contrast-enhanced cancerous particles. The contour map of the fluorescence image after this processing step is depicted in Figure 8c. The cancerous cells (bright red color segments) have been accurately assigned and depicted within the ground truth tumor borders. In addition, outside the marked tumor area, the method has also assigned tumorous kernels, which are not significant in the present tumor formation but may be considered suspicious for subsequent examination. From the qualitative results presented, a key observation is that images processed utilizing clustering-based segmentation demonstrate increased contrast compared to raw ones. Otsu thresholding enhanced the differences between the foreground (tumor margins) and the background (healthy and remaining tissue), facilitating the segmentation procedure. Entropy thresholding also provided promising results, enabling the accurate definition of tumor borders via edge enhancement and efficiently identifying the object pixels, depicted in the high density of the 3D intensity maps shown. Finally, mean shift clustering smoothens image data, providing additional information from the probabilistic distribution of the fluorescence samples, complementing the final result, and removing possible outliers introduced through the thresholding procedure. The contour maps were also plotted for the images obtained using entropy thresholding, for an enhanced visual comparison. The binary mask obtained by fusing all three techniques and filling any gaps via mathematical morphology is also shown for visualizing the entire tumor boundary. It is also interesting to note that the tumor can be considered as the bright region positioned  
Figure 10: Fluorescence images containing the brain tissue at 120 μm and 150 μm slice depth along with the ground truth tumor margins (red-colored boundary): (**a**) Original image at 120 μm slice depth, (**b**) original image at 150 μm slice depth, (**c**) ground truth (red) and estimated (blue) tumor margins superimposed on the original fluorescence image margins after the application of the proposed unsupervised learning segmentation scheme on the image of Figure 10a, and (**d**) ground truth (red) and estimated (blue) tumor margins superimposed on the original fluorescence image after the application of the proposed unsupervised learning segmentation scheme on the image of Figure 10b. at the top of the image and the high intensity (intensity = 1) points depicted in the 3D intensity map of the image histogram. The proposed test bed fluorescence imaging system proves quite robust, being capable of recognizing extremely irregular-shaped tumors, in an unsupervised and almost real-time manner and without any prior knowledge of the tissue properties, serving as a very helpful assisting tool for the pathologist. In addition, the improved contrast produced by the entropy thresholding process enables more accurate differentiation of the tumor area from the remaining tissue and, in conjunction with the result of Otsu thresholding, filters out the ambiguous pixels present in the union area between the ROI (Region of Interest) and the background. Subsequently, the mean shift clustering sub-procedure enables the derivation of quite clear and smooth tumor margins.