Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images

Jingjing Song; Qingjie Zhao; Yuanquan Wang; Jie Tian

doi:10.1007/11801603_169

Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images

Jingjing Song^*
, Qingjie Zhao
, Yuanquan Wang
, Jie Tian

^*Corresponding author for this work

School of Computer Science and Technology

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

3 Citations (Scopus)

Abstract

In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration, We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images.

Original language	English
Title of host publication	PRICAI 2006
Subtitle of host publication	Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Publisher	Springer Verlag
Pages	1242-1247
Number of pages	6
ISBN (Print)	3540366679, 9783540366676
DOIs	https://doi.org/10.1007/11801603_169
Publication status	Published - 2006
Event	9th Pacific Rim International Conference on Artificial Intelligence - Guilin, China Duration: 7 Aug 2006 → 11 Aug 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4099 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Pacific Rim International Conference on Artificial Intelligence
Country/Territory	China
City	Guilin
Period	7/08/06 → 11/08/06

Access to Document

10.1007/11801603_169

Cite this

Song, J., Zhao, Q., Wang, Y., & Tian, J. (2006). Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images. In PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 1242-1247). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4099 LNAI). Springer Verlag. https://doi.org/10.1007/11801603_169

Song, Jingjing ; Zhao, Qingjie ; Wang, Yuanquan et al. / Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images. PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2006. pp. 1242-1247 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{515bff53301f42eca9394027f5b78a00,

title = "Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images",

abstract = "In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration, We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images.",

author = "Jingjing Song and Qingjie Zhao and Yuanquan Wang and Jie Tian",

year = "2006",

doi = "10.1007/11801603\_169",

language = "English",

isbn = "3540366679",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1242--1247",

booktitle = "PRICAI 2006",

address = "Germany",

note = "9th Pacific Rim International Conference on Artificial Intelligence ; Conference date: 07-08-2006 Through 11-08-2006",

}

Song, J, Zhao, Q, Wang, Y & Tian, J 2006, Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images. in PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4099 LNAI, Springer Verlag, pp. 1242-1247, 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, 7/08/06. https://doi.org/10.1007/11801603_169

Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images. / Song, Jingjing; Zhao, Qingjie; Wang, Yuanquan et al.
PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2006. p. 1242-1247 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4099 LNAI).

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

TY - GEN

T1 - Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images

AU - Song, Jingjing

AU - Zhao, Qingjie

AU - Wang, Yuanquan

AU - Tian, Jie

PY - 2006

Y1 - 2006

N2 - In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration, We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images.

AB - In this paper, we present a new and fast algorithm of fuzzy segmentation for MR image, which is corrupted by the intensity inhomogeneity. The algorithm is formulated by modifying the FFCM algorithm to incorporate a gain field, which compensate for such inhomogeneities. In each iteration, we allow the gain field transforming to a gain field image and filter it using an iterative low-pass filter, and then revert the gain field image to gain field term again for the next iteration, We also use c-means algorithm initializing the centroids to further accelerate our algorithm. Our method reduces lots of executive time and will obtain a high-quality result. The efficiency of the algorithm is demonstrated on different magnetic resonance images.

UR - https://www.scopus.com/pages/publications/33749553134

U2 - 10.1007/11801603_169

DO - 10.1007/11801603_169

M3 - Conference contribution

AN - SCOPUS:33749553134

SN - 3540366679

SN - 9783540366676

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1242

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BT - PRICAI 2006

PB - Springer Verlag

T2 - 9th Pacific Rim International Conference on Artificial Intelligence

Y2 - 7 August 2006 through 11 August 2006

ER -

Song J, Zhao Q, Wang Y, Tian J. Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images. In PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2006. p. 1242-1247. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11801603_169

Gain field correction fast fuzzy c-means algorithm for segmenting magnetic resonance images

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