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Automated Recursive Segmentation of Large Neocortical Images Using Standard Deviation as Termination Criteria
Konkachbaev, A.I. Casanova, M.F. Graham, J.H. Elmaghraby, A.S.
Compur Eng. & Compteuter Sci., Louisville Univ., KY;

This paper appears in: Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Publication Date: 17-18 Jan. 2006
On page(s): 2531-2534
Location: Shanghai,
ISBN: 0-7803-8741-4
INSPEC Accession Number: 9119951
Digital Object Identifier: 10.1109/IEMBS.2005.1616984
Current Version Published: 2006-04-10

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

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