NDLI: Fast tissue segmentation based on a 3D and 4D feature map: comparison study

Content Provider	IEEE Xplore Digital Library
Author	Vinitski, S. Mohamed, F. Gonzalez, C. Knobler, R. Andrews, D. Iwanaga, T. Madi, S.
Copyright Year	1997
Description	Author affiliation: Thomas Jefferson Univ., Philadelphia, PA, USA (Vinitski, S.)
Abstract	The aim of this work was to develop a fast and accurate method for tissue segmentation in MRI based on 4D feature map and compare it with that derived from the 3D feature map. High resolution MR imaging was performed in 5 normals, 6 patients with brain MS, and 6 with malignant brain tumors. Three inputs: proton density, or weighted fast spin-echo, T1-weighted spin echo MR images were routinely utilized. As a fourth input, either magnetization transfer MRI was used in normals and some patients or Tl weighted post contract MRI in other patients. Modified k-Nearest Neighbor segmentation algorithm was optimized for maximum computation speed and high quality segmentation. In that regard: 1) the authors discarded the redundant seed points, 2) discarded the points within 0.5 standard deviation from the cluster center that were non overlapping with other tissue classes, 3) they removed outlying seed points outside 5 times the standard deviation from the cluster center of each tissue class. Their new technique utilizing all 4 MRI inputs provided better segmentation than that based on three inputs. (p<0.001) The tissues were smoother and the delineation of the tissues was increased. Details that were previously blurred or invisible now became apparent. In normals, detailed depiction of deep gray matter nuclei was obtained. In malignant tumors, up to 5 abnormal tissues were identified: 1) solid tumor core, 2) cyst, 3) edema in white matter 4) edema in gray matter and 5) necrosis. Delineation of MS plaque in different stages of demyelination, became much sharper. In conclusion, proposed methodology warrants further development and clinical evaluation.
Starting Page	505
Ending Page	508
File Size	648714
Page Count	4
File Format	PDF
ISBN	0780342623
ISSN	1094687X
DOI	10.1109/IEMBS.1997.757656
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-10-30
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Magnetic resonance imaging Neoplasms Image resolution High-resolution imaging Cancer Protons Magnetization Contracts Clustering algorithms
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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