COVID-index: A texture-based approach to classifying lung lesions based on CT images
作者：
Highlights：
• Our method takes into account the VOIs of the TCs, without the need to divide them into slices, i.e, we analyze the entire volume.
• We propose eight image texture descriptors, which do not require parameterization and not need to resize images.
• We have developed a scalable method, since it can be easily used in 2D or 3D images, without restrictions regarding the quantization of images.
• Our descriptors can achieve results, as promising as deep networks, in some cases, with superior results.
• Our descriptors, too, do not need large amounts of images to achieve good results.
摘要
•Our method takes into account the VOIs of the TCs, without the need to divide them into slices, i.e, we analyze the entire volume.•We propose eight image texture descriptors, which do not require parameterization and not need to resize images.•We have developed a scalable method, since it can be easily used in 2D or 3D images, without restrictions regarding the quantization of images.•Our descriptors can achieve results, as promising as deep networks, in some cases, with superior results.•Our descriptors, too, do not need large amounts of images to achieve good results.
论文关键词：COVID-19,Computed tomography,3D texture analysis,Phylogenetic diversity
论文评审过程：Received 14 October 2020, Revised 22 May 2021, Accepted 27 May 2021, Available online 6 June 2021, Version of Record 15 June 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108083
