## 3 Proposed Tumor Visualization and Identification Framework
Regarding the procedure for brain tumor implants, the animal population was anesthetized and glioblastoma cells were deposited in the right striatum at a slow rate of 1 mL/min and at a depth of 23 mm from the dura. Post imaging brain slices were fixed in a solution medium of 4% paraformaldehyde, 30% sucrose protected, and physically firmed in optimum cutting temperature compound (OCT) for cryostat sectioning (instrument model Leica CM3050S). Tissue sections were acquired sequentially at 10 or 25 mm cut directly onto slides and preserved at \(-80\,\mathrm{\SIUnitSymbolCelsius}\) until manipulation. In order to be visualized via probes, biological material was heated to room temperature for a time interval of 10 min, washed  
Figure 1: The optical fluorescence microscopy arrangement. in phosphate buffer solution (PBS) and cover slipped with fluorescent mounting media. As a final step, fluorescent microscopy images were acquired and analyzed performing an unsupervised clustering framework combined with adaptive thresholding techniques. The proposed tumor identification and visualization pipeline of algorithms are shown in Figure 2. It is based on a three-step framework run in sequential mode, i.e., image processing/segmentation, image fusion, and image refinement modules, each one with a unique contribution to information mining. To quantitatively evaluate the detection accuracy of our fluorescence tumor imaging and identification system, a ground truth image was generated, superimposing the assigned tumor margins marked by a medical expert on the raw fluorescence sample image. Since the accurate detection of tumor margins is impossible, even with dense sampling and biopsy, the validation of the extracted results is performed based on a closed and compact region approximation of the overall tumor surrounding contour produced by the pathologist. Thus, we should note here that the following study is semi-quantitative, as it is affected by the qualitative judgment of the expert. Thus, our evaluation at this stage considers the agreement between the algorithmic and the medical perspectives on the significant tumor area, its extent, and structure. As already mentioned, assurance about the local condition can only be provided by selective biopsy. In our study, the biopsy was performed at three specific small areas in the region of strongest fluorescent response (this biopsy was guided by the proposed imaging modality), which verified the tumorous nature of all tested sites. The algorithmic identification (segmentation) procedure, the first step of the proposed tumor visualization pipeline, is initiated by performing median filtering (\(3\times 3\) window size) on the original fluorescence image in order to eliminate speckle noise, possibly introduced during the image acquisition procedure. The image processing module is gradually performed in three discrete stages run in parallel: an initial estimation of tumor region is performed by applying the Otsu thresholding technique; meanwhile, an enrichment step  
Figure 2: Schematic representation of the discrete steps in the tumor identification and visualization pipeline. takes place via the entropy thresholding approach, which introduces information regarding the background area and refined borders of the cancerous region. At the third branch of the image processing module, the smoothed key segments (those that differentiate most from the background) of the tumor margins are estimated performing the mean shift clustering methodology to the fluorescence sample image.