Investigation of Significant Attributes Based on Image Feature and Texture Analysis for Automatic Noise Reduction in MRI

Abstract

In 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 features and textures to facilitate subsequent automation process. In our approach, a total number of 60 image attributes are considered that are based on three categories: 1) Image statistics. 2) Gray-level cooccurrence matrix (GLCM). 3) 2-D discrete wavelet transform (DWT). To obtain the most significant attributes, a t-test is applied to each individual image features computed in every image. The evaluation is based on the distinguishing ability between noise levels, intensity distributions, and anatomical geometries. We have adopted the BrainWeb image data with various levels of noise and intensity non-uniformity to evaluate our methods. Finally, we report the most significant attributes in ascending order based on the p-value and suggest the use of these features for the automation process.