# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 1 Introduction  
The image classification domain has received a lot of interest from researchers due to its significant contribution and successful implementation in different applications from the biomedical domain, remote sensing, industry and many more. The task of image classification involves the prediction of a class from a set of predefined categories for a given input image by taking into consideration its visual content [1]. Even if the components of an image classification system depend on the specific application in which the system is used, two processes are always needed in any supervised machine-learning image classification task: training and testing. The training step uses a training set of images for which the categories to which they belong are known. These images are analysed by using a feature extraction technique, which is used to provide the most important and discriminative features in an image (stored as a feature vector). In the testing step, as input for the classification system, a new image is used, and the goal of this step is to predict the membership class of that image [2]. The same feature extraction method is used to determine the feature vector for the new image. At this point, a classification operation is used to compare this feature vector with the ones obtained in the training phase and to assign the new image to the nearest class based on a specific criterion. The feature extraction step is the most important in such a task since the machine-learning algorithm operates on the extracted features [3]. The image analysis process is used to obtain a reduced representation of the images by keeping only the most important information. If the features are not sufficiently relevant and discriminative, even themost powerful machine-learning algorithm cannot achieve a good performance. The discrimination power is gained by descriptors that provide low intra-class variability and high variability between different classes [4]. The image classification task is a challenging one because images are exposed to different illumination conditions, can be affected by noise and can suffer different transformations. That is why the extracted features should provide invariance and robustness to these conditions in order to achieve a good classification performance. One of the most important characteristics of images is the texture. This is true especially for medical images where the textural features and the statistical indicators (such as kurtosis, entropy) are the most relevant for the detection of several diseases, such as breast cancer [5]. The textural description of images was implied in many feature extraction techniques proposed in literature. Most of them were mainly focused on 2D textured images. Volumetric image analysis and classification is of interest nowadays due to the progress gained concerning 3D sensor technologies and imaging techniques [6]. In the medical domain, 3D images are widely used in computer-aided diagnostic systems that are based on images acquired, for example, by magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound techniques. Such volumetric images are represented as data cubes, so feature extraction methods should be adapted to deal with this type of data. Since texture is a very important feature of medical images, powerful and discriminative texture feature extraction methods are needed to achieve a reliable classification of such images, taking into consideration that for volumetric images, the computation complexity is a challenging problem. Due to the progress gained in the 3D imaging domain, the interest regarding volumetric texture feature analysis methods increased. Even if 2D feature extraction methods can still be applied on slices of 3D images, this strategy would disregard the relations between slices. The disadvantage of such an approach is the information loss in the propagation of texture along the neighbouring slices. The 3D feature extraction field is significantly less developed than the 2D one.