DocumentCode
1615681
Title
Automated Recursive Segmentation of Large Neocortical Images Using Standard Deviation as Termination Criteria
Author
Konkachbaev, A.I. ; Casanova, M.F. ; Graham, J.H. ; Elmaghraby, A.S.
Author_Institution
Compur Eng. & Compteuter Sci., Louisville Univ., KY
fYear
2006
Firstpage
2531
Lastpage
2534
Abstract
In this paper we present an improved segmentation algorithm that recursively explores various thresholding levels until it reaches a termination criteria. This segmentation algorithm is based on earlier work adapting Otsu´s thresholding approach to myelinated bundles of axons in cortical tissue. Experimentation using over 120 images has confirmed that this termination criteria provides visibly acceptable segmentation in an automated fashion
Keywords
biomedical MRI; brain; cellular biophysics; image segmentation; medical image processing; neurophysiology; MRI; automated recursive segmentation; axons; cortical tissue; large neocortical images; myelinated bundles; standard deviation; termination criteria; thresholding approach; Biomedical image processing; Biomedical optical imaging; Computer science; Histograms; Image segmentation; Lung neoplasms; Magnetic resonance imaging; Nerve fibers; Pixel; Psychiatry;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Conference_Location
Shanghai
Print_ISBN
0-7803-8741-4
Type
conf
DOI
10.1109/IEMBS.2005.1616984
Filename
1616984
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