## 4 Results and Discussion
### Evaluation of Detection Results  
Figures 6-10 illustrate the raw fluorescence images containing the brain tissue in various slice depths (10, 30, 60, 100, 120, and 150 ) along with the estimated tumor margins after the application of the proposed unsupervised learning segmentation scheme, using the three-step imaging algorithm. The ground truth marked tumor borders designed by the medical expert are superimposed in each image and highlighted with a red-colored curve, constituting the expert approximation to be attained and reflecting a base of quantitative measurements of the accuracy of our proposed scheme. The algorithmically derived area in each figure is indicated by the compact white regions, discriminating the cancerous cell segments from the remaining tissue and the background. The estimated tumor borders and tumor segments are presented at several stages of processing. The results from the individual segmentation approaches are first  
Figure 5: A comparison of various thresholding-based image segmentation techniques for the image of slice depth 10 : (**a**) entropy thresholding (selected method), (**b**) minimum thresholding, (**c**) intermodes thresholding, (**d**) IsoData thresholding, (**e**) Li thresholding, (**f**) Shanbhag thresholding. The blue curve indicates the borders of brain tissue depicted in the image. presented, revealing the different aspects of information that can be extracted. The extracted binary mask representing the overall brain tumor region is depicted as a compact white area corresponding to the geometrical topology of the cancerous tissue. Finally, the algorithmically derived tumor borders are depicted with a blue color, so they can be compared with the expert borders and used as quantitative metrics of the identification efficiency. Notice that both the geometry and the dimensions of the tumorous area have been reliably reconstructed, emphasizing the potential of the proposed fluorescent imaging system. We note the efficiency of the mean shift clustering sub-procedure to filter out the non-tumorous segments, enhancing the segmentation and margin identification accuracy. The geometry of the cancerous brain tissue has also been correctly reconstructed. 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. Figure 9 depicts our results at a rather large depth of 100 \(\upmu\)m. Along with this experiment, Figure 9b demonstrates the efficiency of Otsu thresholding to provide an initial estimation of the region of interest. This process reveals the cancerous cells within the marked area of interest, but also introduces many other small structures throughout the image, which are due to small intensity deviations and do not reflect information of interest. These structures are eliminated by the subsequent procedure mean shift segmentation. Furthermore, the entropy thresholding in Figure 9c again captures cancerous cells of interest (bright red color segments) with good correspondence to the expert margins. Figure 10 depicts the expert and approximated tumor borders superimposed on two fluorescence images at depths 120 \(\upmu\)m and 150 \(\upmu\)m, respectively. It is important to note how the appearance of irregularly shaped cancerous regions appear in different slice depths, exemplifying how difficult it is for a pathologist or a surgeon to form an accurate estimation of tissue status and tumor spread.