3D Texture Segmentation using Supervised Methods

Abstract

Supervised learning methods have been widely used for image classification in various fields, including medical and industrial sectors. Some of these methods are traditional and possess certain limitations when addressing complex problems. The most common and effective approaches involve Convolutional Neural Networks (CNNs), such as U-Net. However, most studies employ CNNs in their 2D structures, which can impose limitations in classifying 3D objects. The purpose of this paper is to propose the utilization of 3D CNNs to potentially enhance the classification of 3D data, particularly X-ray micro-computed tomography images of reservoir rock samples. Our focus is on examining the performance of the 3D U-Net architecture, a supervised classification approach, in segmenting various 3D rock textures.