## 2 Literature Review
Image and signal generation through the determination of the polarization states of light proves quite effective and sustains specific benefits for a wide variety of identification and classification tasks and applications, mostly based on the intrinsic nature of optical backscattering to provide increased contrast in varying polarization conditions. As a result, under backscattered geometry, multiple kinds of early-stage malignancies (cancer) could be discriminated, considering and quantifying their unique diffuse reflectance polarimetric signatures. Another related method uses polarimetric discrimination of the wavelet-fractal domain for histological analysis of monolayer lung cancer cells [31; 32]. Wavelet polarimetric evaluation of healthy, squamous carcinoma, and adenocarcinoma lung tissue cell lines proves quite promising and reliable in accurately and robustly classifying cells as healthy or cancerous ones, in conjunction with proper discrimination between malignant cells originating from different types of lung cancer [32]. In order to provide efficient interfaces to medical doctors and guide them to easily focus on affected regions, imaging modalities must be supported by digital image processing and segmentation schemes, especially in the case of biomedical applications, as medical images suffer from low contrast and noise. Thus it is of paramount importance to select appropriate and application adaptive approaches in order to increase contrast, decrease information loss and artifacts from captured image data. Histogram-based techniques constitute a common yet quite effective tool for medical image enhancement, finding numerous applications and algorithmic modifications in international literature for many years [33; 34]. The latest advances in magnetic resonance (MR) image enhancement include metaheuristics and particle swarm optimization (PSO). [PERSON] et al. [35; 36] proposed an automatic global thresholding and segmentation framework based on genetic algorithms (GAs), confirming through MRI data that there is an underlying bimodal distribution of pixel intensities. The principal idea lies in calculating the optimal threshold (best solution) under an evolutionary computational approach that better discriminates the two Gaussian distributions, formulating a bimodal pixel intensity representation function (histogram) in a medical image sub-region. [PERSON] and [PERSON] introduced an efficient particle swarm optimized histogram equalization scheme to automatically extract optimal threshold for texture identification of regions through an iterative approach under a fitness function that combines different performance metrics [37]. The main limitation of such approaches is the increased computational and algorithmic design complexity along with increased overall processing time. Machine learning and computational intelligence approaches are outside the scope of the present study, as we focus on developing a framework that is easy for medical experts to use and set up and can be adopted in real- or near real-time image analysis applications. Clustering constitutes a widely-established methodology for statistical image analysis, based on assigning groups of pixels into classes (clusters) based on a distance index/metric (intensity similarity and topological proximity), so that the topological inter-cluster similarity and the extra-cluster separability are increased. The selection of the optimal clustering scheme and algorithmic setup (namely, the feature definition, the initial cluster centers or seed points, the distance function to be adopted, or the number of dominant classes) depends on the input dataset origin and type along with the application requirements. The classic k-means and the mean shift algorithms constitute two fundamental frameworks for image classification and decomposition.