Image segmentation based on scaled fuzzy membership functions

Jan Jantzen; P. Ring,; Pernille Christiansen

doi:10.1109/FUZZY.1993.327401

Image segmentation based on scaled fuzzy membership functions

Jan Jantzen, P. Ring,, Pernille Christiansen

Danish Research Centre for Magnetic Resonance

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician

Original language	English
Title of host publication	Proceedings of the 2nd IEEE International Conference on Fuzzy Systems
Volume	Volume 2
Publisher	IEEE
Publication date	1993
Pages	714-718
ISBN (Print)	07-80-30614-7
DOIs	https://doi.org/10.1109/FUZZY.1993.327401
Publication status	Published - 1993
Event	IEEE International Conference on Fuzzy Systems - San Francisco, CA Duration: 1 Jan 1993 → … Conference number: 2nd

Conference

Conference	IEEE International Conference on Fuzzy Systems
Number	2nd
City	San Francisco, CA
Period	01/01/1993 → …

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Copyright 1993 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

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title = "Image segmentation based on scaled fuzzy membership functions",

abstract = "As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician",

author = "Jan Jantzen and P. Ring, and Pernille Christiansen",

note = "Copyright 1993 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.; IEEE International Conference on Fuzzy Systems ; Conference date: 01-01-1993",

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AB - As a basis for an automated interpretation of magnetic resonance images, the authors propose a fuzzy segmentation method. The method uses five standard fuzzy membership functions: small, small medium, medium, large medium, and large. The method fits these membership functions to the modes of interest in the image histogram by means of a piecewise-linear transformation. A test example is given concerning a human head image, including a sensitivity analysis based on the fuzzy area measure. The method provides a rule-based interface to the physician

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DO - 10.1109/FUZZY.1993.327401

M3 - Article in proceedings

SN - 07-80-30614-7

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BT - Proceedings of the 2nd IEEE International Conference on Fuzzy Systems

PB - IEEE

T2 - IEEE International Conference on Fuzzy Systems

Y2 - 1 January 1993

ER -

Image segmentation based on scaled fuzzy membership functions

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