NDLI: Contour Detection of Labelled Cellular Structures from Serial Ultrathin Electron Microscopy Sections using GAC and Prior Analysis

Content Provider	IEEE Xplore Digital Library
Author	Huaizhong Zhang Morrow, P. McClean, S. Saetzler, K.
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. & Inf. Eng., Univ. of Ulster, Coleraine (Huaizhong Zhang; Morrow, P.; McClean, S.) \|\| Sch. of Biomed. Sci., Univ. of Ulster, Coleraine (Saetzler, K.)
Abstract	In this paper we discuss how the classical geodesic active contours (GAC) model is enhanced by incorporating `prior' information into the scheme. The modified model is applied to biomedical imagery, specifically serial ultrathin electron microscopy sections. The approach used is to apply prior analysis on a training set of data and provide geometric information about the target object during the process of curve evolution. The experimental results and analysis for both synthetic and real images show that the approach performs better than our previous method. It can be implemented semi-automated fashion giving significant improvements compared to a manual approach.
Starting Page	1
Ending Page	7
File Size	1110033
Page Count	7
File Format	PDF
ISBN	9781424433216
DOI	10.1109/IPTA.2008.4743746
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-11-23
Publisher Place	Tunisia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Level set Image processing GAC Biomedical computing Prior Analysis Electron microscopy Geophysics computing Information analysis Active contours Image analysis Boundary Detection Prior Information Curve Evolution Biomedical imaging Biomedical engineering
Content Type	Text
Resource Type	Article

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