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.
Methods: In our approach, a total number of 60 image texture attributes are considered that are based on three categories: 1) Image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) 2-D discrete wavelet transform (DWT). To obtain the most significant attributes, a paired-samples 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.
Results: A wide variety of images were adopted including the Brain Web image data with various levels of noise and intensity non-uniformity to evaluate the proposed methods. Experimental results indicated that an optimal number of seven image features performed best in distinguishing MR images with various combinations of noise levels and slice positions. They were the contrast and dissimilarity features from the GLCM category and five norm energy and standard deviation features from the 2-D DWT category.
Conclusions: We have introduced a new framework to systematically investigate significant attributes from various image features and textures for the automation process in denoising MR images.Sixty image texture features were computed in every image followed by a paired-samples t-test for the discrimination evaluation. Seven texture features with two from the GLCM category and five from the 2-D DWT category performed best, which can be incorporated into denoising procedures for the automation purpose in the future.
Keywords:
Published on: May 2, 2015 Pages: 1-5
Full Text PDF
Full Text HTML
DOI: 10.17352/pjbe.000001
CrossMark
Publons
Harvard Library HOLLIS
Search IT
Semantic Scholar
Get Citation
Base Search
Scilit
OAI-PMH
ResearchGate
Academic Microsoft
GrowKudos
Universite de Paris
UW Libraries
SJSU King Library
SJSU King Library
NUS Library
McGill
DET KGL BIBLiOTEK
JCU Discovery
Universidad De Lima
WorldCat
VU on WorldCat
PTZ: We're glad you're here. Please click "create a new query" if you are a new visitor to our website and need further information from us.
If you are already a member of our network and need to keep track of any developments regarding a question you have already submitted, click "take me to my Query."