Segmentation of rat brain MR images using a hybrid fuzzy system

Chih Wei Chang; Gilbert R. Hillman; Hao Ying; Thomas A. Kent; John Yen

Segmentation of rat brain MR images using a hybrid fuzzy system

Chih Wei Chang, Gilbert R. Hillman, Hao Ying, Thomas A. Kent, John Yen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one `unclassified' class. In the second stage, the classified result of the first stage is used to find the initial prototypes for FCM and the `unclassified' pixels are classified by FCM. The result of this combination is a very robust classification system. Rat brain MR images with stroke lesions are segmented. This system successfully identified the penumbra area of the rat brain.

Original language	English (US)
Title of host publication	Proc 1994 1 Int Jt Conf NAFIPS IFIS NASA
Editors	Larry Hall, Hao Ying, Reza Langari, John Yen
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	55-59
Number of pages	5
State	Published - 1994
Event	Proceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA - San Antonio, TX, USA Duration: Dec 18 1994 → Dec 21 1994

Other

Other	Proceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA
City	San Antonio, TX, USA
Period	12/18/94 → 12/21/94

ASJC Scopus subject areas

General Engineering

Cite this

Segmentation of rat brain MR images using a hybrid fuzzy system. / Chang, Chih Wei; Hillman, Gilbert R.; Ying, Hao et al.
Proc 1994 1 Int Jt Conf NAFIPS IFIS NASA. ed. / Larry Hall; Hao Ying; Reza Langari; John Yen. Institute of Electrical and Electronics Engineers Inc., 1994. p. 55-59.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chang, CW, Hillman, GR, Ying, H, Kent, TA & Yen, J 1994, Segmentation of rat brain MR images using a hybrid fuzzy system. in L Hall, H Ying, R Langari & J Yen (eds), Proc 1994 1 Int Jt Conf NAFIPS IFIS NASA. Institute of Electrical and Electronics Engineers Inc., pp. 55-59, Proceedings of the 1994 1st International Joint Conference of NAFIPS/IFIS/NASA, San Antonio, TX, USA, 12/18/94.

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title = "Segmentation of rat brain MR images using a hybrid fuzzy system",

abstract = "We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one `unclassified' class. In the second stage, the classified result of the first stage is used to find the initial prototypes for FCM and the `unclassified' pixels are classified by FCM. The result of this combination is a very robust classification system. Rat brain MR images with stroke lesions are segmented. This system successfully identified the penumbra area of the rat brain.",

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PY - 1994

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N2 - We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one `unclassified' class. In the second stage, the classified result of the first stage is used to find the initial prototypes for FCM and the `unclassified' pixels are classified by FCM. The result of this combination is a very robust classification system. Rat brain MR images with stroke lesions are segmented. This system successfully identified the penumbra area of the rat brain.

AB - We have developed a magnetic resonance (MR) image segmentation system which consists of a fuzzy ruled-based system and a fuzzy c-means algorithm (FCM). The first stage of the system is the fuzzy ruled-based system which classifies most pixels of MR images into several known classes and one `unclassified' class. In the second stage, the classified result of the first stage is used to find the initial prototypes for FCM and the `unclassified' pixels are classified by FCM. The result of this combination is a very robust classification system. Rat brain MR images with stroke lesions are segmented. This system successfully identified the penumbra area of the rat brain.

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M3 - Conference contribution

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ER -

Segmentation of rat brain MR images using a hybrid fuzzy system

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