Investigation of Significant Features Based on Image Texture Analysis for Automated Denoising in MR Images

Introduction: In magnetic resonance (MR) image analysis, noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing such as tissue classification, segmentation and registration. Consequently, noise removal in MR images is important and essential for a wide variety of subsequent processing applications. In the literature, abundant denoising algorithms have been proposed, most of which require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. However, this will induce another problem of seeking appropriate meaningful attributes among a huge number of image characteristics for the automation process. This paper is in an attempt to systematically investigate significant attributes from image texture features to facilitate subsequent automation processes.