NDLI: Vector Seeded Region Growing for parenchyma classification

Content Provider	IEEE Xplore Digital Library
Author	Cheng, C.H. Lin, G.C. Ju, S.W. Wang, H.C. Wang, C.M.
Copyright Year	2010
Description	Author affiliation: Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, Taiwan (Cheng, C.H.; Ju, S.W.; Wang, H.C.) \|\| Department of Electrical Engineering, National Central University, Chungli, Taiwan (Lin, G.C.) \|\| Department of Computer Science and Information Engineering, National Chin-Yi University, of Technology, Taichung, Taiwan (Wang, C.M.)
Abstract	Magnetic Resonance Imaging (MRI), being noninvasive and having no radiation hazards, provides abundant tissue information and has become an efficient instrument for clinical diagnoses and research in recent years. When tissues are classified by means of MRI, the images are multi-spectral. Therefore, if only a single image with a certain spectrum is processed, the goal of tissue classification will not be achieved because the single image can't provide adequate information. Consequently, it is necessary to integrate the information of all the spectral images to classify tissues. Multi-spectral image processing techniques [1–3] are hence employed to collect spectral information for classification and of clinically critical values. Based on brain MRI, this study applied Unsupervised Vector Seeded Region Growing (UVSRG) to classification, and the result indicating the possible usefulness of this method..
Starting Page	721
Ending Page	724
File Size	791277
Page Count	4
File Format	PDF
ISBN	9781424469826
e-ISBN	9788988678176
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-11
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	AICIT
Subject Keyword	Multispectral imaging Instruments Merging MRI Hazards Computer science Image segmentation UVSRG Magnetic resonance imaging Clustering algorithms Euclidean distance Classification Pixel
Content Type	Text
Resource Type	Article

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