# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 1 Introduction
This allows the advantage of treating a group of pixels that are sufficiently similar as a single pixel, which aids in processing. The number of superpixels that are created from an image can be adjusted, just as the size and number of connected pixels that comprise a superpixel [11]. The preliminary algorithms for generating superpixels have been criticized as limited and inefficient, so recently methods have been explored to limit the drawbacks. The method of simple linear iterative clustering (SLIC) was proposed to improve efficiency and segmentation performance. This method consists of a modification of the k-means clustering where the cluster assignment of each pixel is limited to a local pixel neighborhood region to generate the superpixels [12]. Traditionally, superpixel methods were explored for RGB images. However, the method can also be incorporated to HSI as well to group pixels that are spatially connected with similar spectral features. In geosciences and remote sensing, the concept of superpixels has been recently applied to standard HS datasets, such as Indian Pines or the University of Pavia datasets, with success at segmentation of earth scenes [13; 14; 15; 16; 17]. Additionally, the processing method has been applied to problems in medicine. [PERSON] et al. combined superpixel algorithms with supervised machine learning utilized for segmentation of head and neck cancers using gross-level HSI of mice [18]. In standard RGBdigital histology, [PERSON] et al. employed multi-resolution generated superpixels for classification of whole-slide RGB digital histology images [19]. Superpixel approaches have also been applied in the context of oncological histology. [PERSON] et al. combined DL and superpixel techniques to detect tumor-infiltrating immune cells in breast histological slides [20], while [PERSON] et al. exploited superpixel features with Markov Random Fields for the diagnosis of melanoma histological samples [21]. Previously, we demonstrated glioblastoma (GB) tumor (a malignant high-grade glioma of the central nervous system) detection in pathological slides using HSI and patch-based CNNs [10]. In CNNs, both spectral and spatial information are used to learn the differences between tumor and non-tumor samples, but the patch-based regions were limited to square patches, which may group together regions of distinct micro-anatomical structures within the same patch. Therefore, in this work, we propose to further investigate the effectiveness of the exclusive exploitation of the spectral information within HS images. The novel contributions of this work are as follows. First, we explore superpixels for region-based grouping of similar pixels using both spatial and spectral information in HS digital histology images from GB tumor patients. For superpixel generation, we utilized the original SLIC algorithm, but modified the distance metric to be more suitable for spectral data. Next, the objective of this work is to employ only the spectral information from superpixels for supervised classification for GB tumor detection. To the best of our knowledge, this is the first work to combine HS digital histology and superpixel algorithms. The results of this investigation will help inform the use of supervised machine learning algorithms in the field of HS digital histology.