## 2 Literature Review
The first one (known as \"k-means clustering\") [38]is an iterative approach that aims to divide a set of data samples (pixel intensities in the case of images) into K groups (clusters) by exploiting the data-driven probability density function in the topology of the feature space. The algorithm converges to a preset number K of clusters in the data distribution, but the quality of the extracted outcome and classification performance are strongly affected and controlled by the initialization condition and the number of clusters a priori defined. The mean shift (MSH) technique (known as \"mean shift clustering\") [39; 40] is considered an identical solution to overcome the previously mentioned key drawback of k-means clustering. MSH is a robust, statistical, nonparametric detection methodology that works in the density distribution space. Its basic ideas lie in determining and controlling the mode (the highest density of data points) adaptation in terms of a kernel-weighted mean value (average) estimate of data points (observations) within a region of movement (sliding smoothing window). The computational procedure is performed iteratively until convergence to a global or local solution (local density mode) according to a well-defined and carefully selected stopping criterion. Another type of segmentation technique that directly utilizes the intensity distribution of data (histogram) defines thresholds for classes based on certain discrimination criteria. Otsu thresholding has been one of the standard thresholding techniques used for segmenting an image into two key principal regions (object and background classes) [41]. It is based on the Gaussian modeling of each histogram mode as a natural model of the probability distribution function of a physical object. The margin of the Bayesian detection error of a bi-modal joint distribution defines the appropriate threshold, as a balance point between the misdetection probabilities of the two classes. Notice that the approach can be easily extended to the multiclass case by modeling a multi-modal data distribution and defining the appropriate threshold between consecutive pairs of Gaussian models. On similar grounds, entropy-based thresholding exploits the Shannon entropy [42] of distribution in each histogram mode and interprets the maximization of the thresholded image entropy as indicative of maximum information transfer [43]. These techniques were used in this study to threshold the fluorescence images and discriminate the cancerous regions from the healthy area of the brain tissue; [PERSON]'s approach is selected because it calculates histograms in a probabilistic manner and produces instant segmentation results under the assumption of the normality of illumination without any prior knowledge of the image properties. In addition, we employ the mean shift procedure in order to refine the specification of image modes within the data distribution and regulate the derivation of empirical probability density functions. As a complementary function, entropy thresholding is also selected to address the information content and interaction of classes. The latter scheme has proven effective in locating sharp discontinuities (edges) within an image in a robust and flexible way, overcoming the sensitivity to noise and time complexity of gradient-based edge detectors utilizing first and second-order derivatives [44]. We expect that this efficiency will be beneficial for the accurate separation of healthy and tumor tissue regions, with well-defined borders, for real-time image-guided surgery. For the efficient segmentation of fluorescence tissue images addressing the sensitivity close to the class borders, we implement the fusion of the previous approaches by means of the union of their results. In particular, the entropy-based result is more closely examined for the discrimination of small cancerous formations in the healthy tissue, whereas the combination of individual results is enhanced by mathematical morphology into closed sections for deriving the gross total area of cancer.