## 2 Materials and Methods
### Traditional Texture Segmentation Algorithms  
For this paper, we compared the results of the following texture segmentation algorithms: co-occurrence matrices [5], watershed [51], local binary patterns (LBP) [52; 53], filtering [54] and multiresolution sub-band filtering (MSBF) [8] against a U-Net architecture [46]. The traditional algorithms have been thoroughly described in the literature; however, for completeness, a short explanation of how features are extracted with each algorithm will follow. For a discussion of traditional texture techniques, the reader is referred to any of the following reviews [58; 59; 60]. Co-occurrence matrices are constructed from a quantised version of a grey level image so that if an image is quantised to 8 levels, the co-occurrence matrix will have 8 rows and columns. The values of each location of the matrix will depend on the number of times that a pair of grey levels jointly occur at a neighbouring distance (e.g., 1 pixel away) with a certain orientation (e.g., horizontally). In this way, a co-occurrence matrix is able to measure local grey level dependence: textural coarseness  
Figure 5: Montages of the texture pairs created to train the deep learning networks. Training images shown in Figures 3 and 4 were tessellated and arranged in diagonal, vertical and horizontal pairs. (**a**) Texture pairs. (**b**) Labels. (**c**) Detail of the texture pairs. (**d**) Detail of the labels. and directionality. For example, in coarse images, the grey level of the pixels change slightly with distance, while for fine textures the levels change rapidly. From this matrix, different features like entropy, uniformity, maximum probability, contrast, difference moment, inverse difference moment and correlation can be calculated [5]. Once the features have been calculated, classifiers can be applied directly, or further processing like the watershed transforms can be applied. Watershed transforms are based on a topographical analogy of a landscape. Should water fall in this landscape, it would find the path through which it could reach a region of minimum altitude, i.e., a basin, sometimes called lake or sea. For each point in the landscape (or pixel of the image) there is a path towards one and only one basin. Thus, the landscape can be partitioned into catchment basins or regions of influence of the regional minima and the boundaries between the basins (e.g., points of inflection) are called the watershed lines. [61]. The watershed transform can be applied to features extracted from the co-occurrence matrix [51]. The basins produced can further be iteratively merged to segment textured regions. Local binary patterns (LBP) [52], explore the relations between neighbouring pixels. These methods concentrate on the relative intensity relations between the pixels in a small neighbourhood and not in their absolute intensity values or the spatial relationship of the whole data. The underneath assumption is that texture is not properly described by the Fourier spectrum and traditional frequency filters. The texture analysis is based on the relationship of the pixels of a \(3\times 3\) neighbourhood. A Texture Unit is first calculated by differentiating the grey level of a central pixel with the grey level of its neighbours. The difference is measured if the neighbour is greater or lower than the central pixel. Two advantages of LBP is that there is no need of quantising images and there is a certain immunity to low frequency artefacts. In a more recent paper, [PERSON] [53] presented another variation to the LBP by considering the sign of the difference of the grey-level differences histograms. Under the new consideration, LBP is a particular case of the new operator called \(p_{8}\). This operator is considered as a probability distribution of grey levels, when \(p(g_{0},g_{1})\) denotes the co-occurrence probabilities, they use \(p(g_{0},g_{1}-g_{0})\) as a joint distribution. Filtering, in the context of image processing, consists of a process that will modify the pixel values.