# 3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors
## 1 Introduction
Most of the 3D feature analysis methods are extensions of efficient and popular 2D descriptors. Such descriptors include the Local Binary Patterns operator [7], which was extended to 3D in [8]. If, in the 2D formulation, each pixel is compared to its neighbours located on a circular neighbourhood, in the 3D extension of LBP addressed in [3], the authors define neighbours on a sphere. Since the dimensionality is a problem in the 3D space, a uniformity criterion is used to reduce the number of possible patterns. In the 2D case, this criterion involves the usage of patterns with at most two bitwise transitions in the pattern. We refer to the original publication, [7], for further details. For volumetric images, the authors propose relaxing the criterion to allow up to three transitions. A 2D LBP-derived operator, the Extended Local Binary Patterns (ELBP), was adapted to deal with volumetric images in [9]. A rotation invariant 3D extension based on the LBP descriptor was introduced in [10] where the authors considered using precomputed 2-patterns. Another efficient texture descriptor besides LBP is the Gray-Level Co-occurrence Matrix (GLCM) which is used as a tool in order to derive second-order statistics from 2D textured images. An extension of GLCM to volumetric images is proposed in [11]. This method is similar to its 2D variant, but the displacement vector is defined by using three coordinates and a higher number of orientations. Another extension to 3D involves the volumetric run-length matrix from which several texture features are used to describe 3D data [12]. Application of volumetric texture analysis methods in the medical domain has become more and more prevalent in recent years. The magnetic resonance imaging (MRI) technique is very important for the medical domain, being a tool used in diagnostic systems. Thus, there is a high interest to develop powerful feature extraction and classification algorithms adapted to this type of images in order to make the distinction between healthy and non-healthy tissue. In [13], the authors propose a feature extraction technique for the classification of magnetic resonance images involving malignant or benign breast lesions. For segmentation of lesions, a fuzzy c-means clustering-based method is used. The features are extracted from a nondirectional GLCM which is obtained by summing all directional GLCMs for all 13 independent directions. In [14], magnetic resonance images of the brain are classified using as feature descriptor multi-sort co-occurrence matrices. This implies considering several axes and combining different features in a multidimensional matrix. In [15], MRI images of knees are analysed by considering a sub-band filtering method. For reducing the size of the feature vector, a feature selection algorithm based on the Bhattacharyya distance measure is proposed. Lately, for the classification of MRI images, different deep-learning-based methods have been proposed in literature, such as [16], where the authors compare three different types of Convolutional Neural Networks (CNN) for the classification of brain MRI images. Another example of brain tumour classification performed on MRI images is [17], where the authors propose a new CNN architecture that can be used to help medical experts to carry out a diagnostic. Other methods used for volumetric image texture feature extraction include using Locally Oriented Wavelet Transforms as in [18]. The three-dimensional Gaussian Markov Random Fields are employed in [19] for the detection of COPD (chronic obstructive pulmonary disease) by considering a dataset containing images of lungs acquired by the computer tomography imaging technique. Another paper that uses Gaussian Markov Random Fields for the analysis of volumetric textures performs the discrimination of soft tissue from abdomen CT images [20]. The paper is structured as follows. Section 2 reviews the original LBP operator along with an improved variant and the 2D and 3D versions of the GLCM feature extraction technique.