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
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;
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
DOI :
10.1109/IEMBS.2005.1616984
