Hostname: page-component-6898cfcb55-zkb6p Total loading time: 0 Render date: 2026-11-11T05:14:04.745Z Has data issue: false hasContentIssue false
  • English
  • Français

An objective method for edge detection in MRI morphometry

Published online by Cambridge University Press:  16 April 2020

MS Keshavan,
C Beckwith,
W Bagwell,
W Pettegrew and
K Ranga Krishnan
MS Keshavan
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA15213
C Beckwith
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA15213
W Bagwell
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA15213
W Pettegrew
Affiliation:
Department of Psychiatry, Western Psychiatric Institute and Clinic, 3811 O’Hara Street, Pittsburgh, PA15213
K Ranga Krishnan
Affiliation:
Department of Psychiatry, Duke University Medical Center, PO Box 3215, Durham, NC27710, USA
Get access

Summary

We describe a simple method to objectively identify the threshold for edge detection in morphometric measurement of brain structures. This method correlates highly with the subjective operator-driven approach to edge detection. While the objective approach, using a histogram of pixel intensities, reduces rater bias and may reliably identify brain-CSF boundaries, its value in morphometric measurements of smaller cerebral structures may be somewhat limited.

Keywords

MRI morphometryedge detectionsegmentation

Information

Type
Short communication
Information
European Psychiatry , Volume 9 , Issue 4 , 1994, pp. 205 - 207
Copyright
Copyright © European Psychiatric Association 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Andreasen, NCO’Leary, DSArndt, SNeuroimaging and clinical neuroscience: basic issues and principles.In: Review of Psychiatry. Vol 12. Washington: American Psychiatric Press, Inc 1993;315-16Google Scholar
Ashtari, MZito, JLGold, BILieberman, JABorenstein, MTHerman, PGComputerized volume measurement of brain structure. Invest Radiol 1990;25:798–803CrossRefGoogle ScholarPubMed
Bland, MAltman, DGStatistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10CrossRefGoogle ScholarPubMed
Cohen, GAndreasen, NCAlliger, RArndt, SKuan, JYuh, WTCEhrhardt, JSegmentation techniques for the classification of brain tissue using magnetic resonance imaging. Psychiatry Res Neuroimaging 1992;45:33–51CrossRefGoogle ScholarPubMed
Jernigan, TLArchibald, SLBerhow, MTSowell, HRFoster, DSHesselink, JRCerebral structure on MRI: Part I. Localization of age-related changes. Biol Psychiatry 1991;29:55–67CrossRefGoogle ScholarPubMed
Pfefferbaum, ALim, KORosenbloom, MZipursky RB, . Brain magnetic resonance imaging: Approaches for investigating schizophrenia. Schizophren Bull 1990;16:453-76CrossRefGoogle ScholarPubMed
Rusinek, Hde Leon, MJGeorge, AEet al. Alzheimer disease: measuring loss of cerebral gray matter with MR imaging. Radiology 1991; 178:109-14CrossRefGoogle ScholarPubMed
Submit a response

Comments

No Comments have been published for this article.