Volumetric Color-Texture Representation for Colorectal Polyp Classification in Histopathology Images

Abstract

With the growth of real-world applications generating numerous images, analyzing color-texture information has become essential, especially when spectral information plays a key role. Currently, many randomized neural network texture-based approaches were proposed to tackle color-textures. However, they are integrative approaches or fail to achieve competitive processing time. To address these limitations, this paper proposes a single-parameter color-texture representation that captures both spatial and spectral patterns by sliding volumetric (3D) color cubes over the image and encoding them with a Randomized Autoencoder (RAE). The key idea of our approach is that simultaneously encoding both color and texture information allows the autoencoder to learn meaningful patterns to perform the decoding operation. Hence, we employ as representation the flattened decoder’s learned weights. The proposed approach was evaluated in three color-texture benchmark datasets: USPtex, Outex, and MBT. We also assessed our approach in the challenging and important application of classifying colorectal polyps. The results show that the proposed approach surpasses many literature methods, including deep convolutional neural networks. Therefore, these findings indicate that our representation is discriminative, showing its potential for broader applications in histological images and pattern recognition tasks.