Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images

Chulhee Lee, Shin Huh, Terence A. Ketter, Michael Unser

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

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 what 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.

Original languageEnglish
Pages (from-to)309-338
Number of pages30
JournalComputers in Biology and Medicine
Volume28
Issue number3
DOIs
Publication statusPublished - 1998 May 1

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Binary images

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Health Informatics

Cite this

Lee, Chulhee ; Huh, Shin ; Ketter, Terence A. ; Unser, Michael. / Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images. In: Computers in Biology and Medicine. 1998 ; Vol. 28, No. 3. pp. 309-338.
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Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images. / Lee, Chulhee; Huh, Shin; Ketter, Terence A.; Unser, Michael.

In: Computers in Biology and Medicine, Vol. 28, No. 3, 01.05.1998, p. 309-338.

Research output: Contribution to journalArticle

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