## 3 Proposed Tumor Visualization and Identification Framework  
This study operates on tissue autofluorescence imaging principles, an optical data acquisition framework that has recently shown promise as a diagnostic modality. Optical fluorescence microscopy is a promising technique for high-quality histological imaging, as it does not harm the tissue samples and saves time and labor [29]. In this experiment, the most common fluorophores within the tissue absorb blue light and re-emit a portion of this radiation at higher wavelengths (green) as a fluorescence signal. Typically, autofluorescence arises from different kinds of fluorophores in the tissue, such as proteins and enzymes; it is sensitive to both morphological, biochemical, and metabolic changes associated with tissue pathologies. Segmentation and enhancement of the obtained images are necessary for the accurate and unsupervised distinction of the tumor margins, even more so, if the background and the object in the image are not very well demarcated due to intensity and texture differences. An unsupervised method aims to minimize errors subjectivity introduced by human intervention/supervision. Optical fluorescence optical data (microscopy images) depicting brain tumor slice samples were acquired via an imaging system designed with a Zeiss laser scanning microscope (LSM), setting up magnification at 5\(\times\). The overall experimental arrangement is shown in Figure 1.  
Samples of human Gli36@EGRF glioblastoma cell lines expressing green fluorescent protein are used throughout this study. Biological material (tumor cells) comes in the form of 10 um tissue slices, which were cut beginning from the initial 2 mm of the former brain section. The dataset includes five slides (each one with three sections), which correspond to the distance (depth) of the slice with respect to the starting point of the anterior brain section (beginning from 10 um and ending to 150 um, in slices of 10 um size). The tumor is positioned on the top boundary of these slices, containing cells that express a green fluorescent protein (GFP). Fluorescence imaging modality is utilized for cancer identification, based on the fact that specific biomarker fluorophores are expressed at a substantially more intensive level in tumor cells with respect to healthy ones, enhancing imaging quality for discrimination purposes. Specifically, brain tumor samples were examined using 488 nm Argon laser attached to the microscope; blue light, after passing through a dichroic lens, is incident on the sample inducing green-light auto fluorescence. Diffuse reflectance-auto-fluorescent images are acquired in the \"green\" spectral range (specific wavelength band selected at 530 nm); while the reflected illumination blue light is blocked with a green-pass filter, set in front of the receiver photomultiplier. Captured images were then processed using unsupervised clustering in conjunction with adaptive thresholding algorithms. The samples were a gift from Dr. [PERSON] [45] and prepared from cloning of human Gli36 cells. The cell lines were cultured in Dulbecco's Modified Eagle Medium (DMEM) (solution contains 4.5 g/L glucose, L-glutamine) supplemented qq2 with 10% concentration in fetal bovine serum (FBS), penicillin levels at 180 U/mL, streptomycin values at 180 mg/mL, and a level of 0.45 mg/mL for amphotericin B (all reagents were developed from Gibco, Invitrogen Corporation). By nature, specific cells overexpress the VIII mutant forms of the estimated glomerular filtration rate (EGFR) gene and have been extensively used for in vivo studies, because they grow rapidly in rat brains. For this purpose, the cell lines were injected into the brain of athymic female nude (nu/nu) mice (6-8 weeks old at the time of surgery), which were grown and preserved at the Animal Resource Center (installations at Case Western Reserve University) in agreement with corresponding institutional policies (CWRU IACUC Animal Experimental Protocol: 2009-0019).