## 1 Introduction
Thus, we do not aim to adopt or develop complex and advanced image processing concepts but mainly focus on the potential of the proposed system (hardware and software) through preliminary laboratory assessments in this area, due to its low cost, simplicity, and time-effective extraction of results. Specifically, this paper addresses the potential of a digital fluorescence imaging system (custom-made design, not a commercially utilized product) supported by an adaptive segmentation pipeline, with the aim of enhancing the imaging and contrast of tumor brain regions. Brain cancer cells were irradiated by an Argon laser operating at 488 nm and connected to the microscope. Output optical data (auto-fluorescence images) were acquired and treated utilizing unsupervised clustering in conjunction with adaptive thresholding algorithms, without any prior knowledge of the sample data. By utilizing the image intensity distribution in three complementary ways and fusing the processing results, the designed system can be viewed as an efficient, user-independent tool for assisting image-guided surgery, providing increased penetration depth and efficiency in locating the tumor margins and area. In this mode, enabling the surgical resection of invasive tumor tissues without causing significant damage to healthy tissue (proposed system follows a non-invasive approach since no straightforward contact between the illumination source and the tissue sample/patient is required), via the accurate specification and extraction of the tumor margins, would yield more effective supplementary therapy and extend patient survival. Focusing on the efficacy of brain tumor resection, the implemented technical and algorithmic framework could be a compact screening and guidance tool, enabling fast, clear identification and the removal of margin-penetrating cells, which could play a key role in future operational and treatment assessments, especially in combination with molecular imaging probes targeting brain tumor markers. The processing unit of the system addresses the joint incorporation of different forms of structural information hidden within image samples via a direct procedure that can be easily understood and adopted by the medical expert. Indeed, the proposed imaging modality enables the facile and non-detrimental application to tissues, in conjunction with accurate and near-instant activation of fluorescence probe, constituting a cost-effective and powerful tool for near real-time visualization and identification of tumor-associated markers during surgery. After the introduction of the aims, novelty, and contribution of the current study, a detailed review of the relevant scientific research is presented in Section 2 in support of the adoption of the proposed optical system design. Section 3 describes the basic concepts of the experimental setup, while Section 4 presents the brain tumor image processing steps. Section 5 illustrates the results of the proposed tumor identification procedure, emphasizing the basic conclusions and prospects. The last section (Conclusions) summarizes the description of the proposed unsupervised learning fluorescence imaging system along with the outcome and potential of the presented work.