# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 2 Materials and Methods
### Texture Analysis  
Texture analysis aims at determining small set of numerical values which clearly characterise the image content clearly. Those features should return similar values for images with the same texture statistics and differ significantly when the image content varies. In this work, we took advantage of the tool qMaZda [43], which allows the calculation of many textural features that exist in the image understanding domain [44]. It allowed us to determine almost 7000 features to describe annotated regions, which additionally considered image normalization and quantization. We examined three options: texture analysis was applied only to the peripheral prostate zone, only to the central zone, and to both regions treated as a whole. The qMaZda software converted the input data into YUV colour space and worked on the illuminance Y channel only. Then, when necessary, the normalization procedure was applied. In the presented research, we calculated the final features for images that were not normalized (D), their grey levels were linearly rescaled to the range of minimal and maximal values (M), or 1 st and 99 th percentiles of grey-level histogram (N), or the grey levels were normalized in range <u \(\pm\) 3\(\sigma\)> (S), where u is the mean illumination while \(\sigma\) stands for standard deviation. In many cases, before the final texture features were calculated, some indirect matrix to compactly represent the image content was created. This matrix resolution is related to the intensity range of pixel values, and the number of data samples that fill it correspond to the number of pixels in the image. Thus, for small images and a large range of pixel intensities, there were not enough data to fill the matrix. It became sparse and the textural features calculated from its content were statistically not certain. Since we did not know whether such a problem occurred, we calculated the textural features for data converted from 5 to 8 bits (hence using matrices from 32 \(\times\) 32 to 256 \(\times\) 256 resolution for data of the same size). To numerically describe the image content, we analysed simple statistics of the pixel illumination values in the image. The first method calculated an image brightness histogram (Hist) from which a basic set of statistical features was determined and reflected the area, mean, variance, skewness, kurtosis, and percentile features of the signal. Next, the rapid changes in illumination were determined in a small neighbourhood. Here, we tried to  
Figure 2: Prostate MRI scan with overlayed manual annotation of the central (blue) and peripheral (orange) zones. decide image contrast by using various approaches to finding edges, starting by gradient map features (Grad) [45], which were transformed to a histogram described by mean, variance, skewness, kurtosis, and non-zero elements. Another approach analyses the influence of pixels places close to each other. The autoregressive model (Arm) [46] searches for the optimal solution, and 4 parameters return information about directionality of the pattern. It can show us in which direction changes are the most popular in the data. The Gabor (Gab) transform analyses the frequency components in the local neighbourhood. It is parametrised by the Gaussian envelope and orientation. thus allowing to settle various frequencies, seen as repetitions of some signal characteristics in one direction. To obtain more information about the edges, the Haar wavelets [47] were applied and the sub-bands energies became the features. As mentioned before, the more complex approaches are characterized by the necessity of creating indirect matrices. In the case of the grey-level co-occurrence matrix (GLCM) [45], the matrix cell counts how many times a pair of pixels of similar illumination values, corresponding to its placement in the matrix, occurs. This approach allowed us to describe some bigger patterns visible in the image.