干旱区生态环境知识资源中心(Arid): Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques

Arid

DOI	10.1109/TLA.2022.9693568
	Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques
	Duarte, Kaue T. N.; Moura, Marinara A. N.; Martins, Paulo S.; de Carvalho, Marco A. G.
通讯作者	Duarte, KTN
来源期刊	IEEE LATIN AMERICA TRANSACTIONS
ISSN	1548-0992
出版年	2022
卷号	20 期号:5 页码:831-838
英文摘要	Brain Imaging has been source of several studies in the literature, mostly due to its importanceboth to predict and to analyze certain diseases or conditions. Extracting the brain from patient images for medical analysis can provide useful diagnostic and prognostic information.To this end, digital image processing algorithms have been applied to medical tasks with a focus on the identification of the brain. This work proposes a brain extraction framework based on three major steps: 1) Dataset and Image Selection; 2) Preprocessing; and 3) Largest Connected Component extraction. Our data are obtained from the OASIS dataset.The preprocessing step is applied in order to enhance contrast and eliminate possible noise from the T1-weighted MRI. Largest Connected Component extraction is performed by initially detecting the largest element in the image (i.e. the brain gray matter) and then by extracting it through mathematical morphology operators. The unsupervised framework extracts the brain in different axial slices without adjustments. The main contribution of this work is a method using only digital image processing for automatically identifying the brain from several different slices, which differs from the literature since is performed without parameter resetting. Five metrics were applied to evaluate our results: Specificity, Recall, Accuracy, F-Measure, and Precision. In our first experiment, two metrics resulted in more than 90% in efficiency (Specificity and Precision), two of them surpassed 80% (F-Measure and Accuracy), and Sensitivity exceeded 70%. Our second experiment compares our results with those produced by related works, having been ranked in the top positions of Sensitivity and Specificity.
英文关键词	Brain Magnetic resonance imaging Histograms Software algorithms Data mining Image edge detection Diseases Image Processing Skull Stripping Brain Extraction Image Segmentation Medical Imaging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000747446400016
WOS关键词	SKULL-STRIPPING PROBLEM
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376188
推荐引用方式 GB/T 7714	Duarte, Kaue T. N.,Moura, Marinara A. N.,Martins, Paulo S.,et al. Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques[J],2022,20(5):831-838.
APA	Duarte, Kaue T. N.,Moura, Marinara A. N.,Martins, Paulo S.,&de Carvalho, Marco A. G..(2022).Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques.IEEE LATIN AMERICA TRANSACTIONS,20(5),831-838.
MLA	Duarte, Kaue T. N.,et al."Brain Extraction in Multiple T1-weighted Magnetic Resonance Imaging slices using Digital Image Processing techniques".IEEE LATIN AMERICA TRANSACTIONS 20.5(2022):831-838.