Automatic Brain Magnetic Resonance Image Denoising Using Texture Feature-Based Artificial Neural Networks
Date Issued
2015
Date
2015
Author(s)
Chang, Yu-Ning
Abstract
Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms 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. In the present study, an artificial neural network associated with texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In our approach, a large number of image attributes are extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. Based on the t-test and the sequential forward floating selection (SFFS) methods, the optimal texture features are selected and incorporated into a back propagation neural network system. We have used a wide variety of simulated T1-weighted MR images and clinical images to evaluate the proposed automatic denoising system. Experimental results indicated that the proposed method accurately predicted the bilateral filtering parameters and automatically removed the noise in a number of MR images with satisfactory quantity and quality.
Subjects
magnetic resonance image
back-propagation neural network
bilateral filter
denoising
image texture feature
t-test
sequential forward floating selection (SFFS)
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-104-R02525056-1.pdf
Size
23.54 KB
Format
Adobe PDF
Checksum
(MD5):a124c859e02bcbd53aa80ed5fdb3b48b