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  4. Unsupervised Connectivity-Based Thresholding Segmentation of Midsagittal Brain MR Images
 
research article

Unsupervised Connectivity-Based Thresholding Segmentation of Midsagittal Brain MR Images

Lee, C.
•
Huh, S.
•
Ketter, T.A.
Show more
1998
Computers in Biology and Medicine

In this paper, we propose an algorithm for automated segmentation of midsagittal brain MR images. First, we apply thresholding to obtain binary images. From the binary images, we locate some landmarks. Based on the landmarks and anatomical information, we preprocess the binary images, which substantially simplifies the subsequent operations. To separate regions that are incorrectly merged after this initial segmentation, a new connectivity-based threshold algorithm is proposed. Assuming that some prior information about the general shape and location of objects is available, the algorithm finds a boundary between two regions using the path connection algorithm and changing the threshold adaptively. In order to test the robustness of the proposed algorithm, we applied the algorithm to 120 midsagittal brain images and obtained satisfactory results.

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Type
research article
DOI
10.1016/S0010-4825(98)00013-4
Web of Science ID

WOS:000076016900008

Author(s)
Lee, C.
•
Huh, S.
•
Ketter, T.A.
•
Unser, M.  
Date Issued

1998

Publisher

Elsevier

Published in
Computers in Biology and Medicine
Volume

28

Issue

3

Start page

309

End page

338

Subjects

MR Images Segmentation

URL

URL

http://bigwww.epfl.ch/publications/lee9802.ps

URL

http://bigwww.epfl.ch/publications/lee9802.html
Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LIB  
Available on Infoscience
November 30, 2005
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/220660
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