Band selection for biomedical hyperspectral data studies using genetic algorithms

Chunni Dai; Qingli Li; Jingao Liu

doi:10.1109/ICBBE.2009.5162435

Band selection for biomedical hyperspectral data studies using genetic algorithms

Chunni Dai^*
, Qingli Li
, Jingao Liu

^*Corresponding author for this work

School of Information and Electronic Engineering (School of Integrated Circuit Science and Engineering)

East China Normal University
Guangxi University for Nationalities

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

4 Scopus citations

Abstract

Hyperspectral imaging coupled with microscopy has been introduced for biomedicine science recently. As hyperspectral data cube include a great deal of single-band images, an adaptive genetic algorithm is presented to solve the best band combination problem for hyperspectral biomedical image studies in this paper. Simulation result demonstrates the effect of this algorithm to the hyperspectral image of leukemia blood cell.

Original language	English
Title of host publication	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs	https://doi.org/10.1109/ICBBE.2009.5162435
State	Published - 2009
Event	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China Duration: 11 Jun 2009 → 13 Jun 2009

Publication series

Name	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Conference

Conference	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Country/Territory	China
City	Beijing
Period	11/06/09 → 13/06/09

Keywords

Band combination
Genetic algorithm
Hyperspectral imaging

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10.1109/ICBBE.2009.5162435

Cite this

@inproceedings{c5c86c2ace1d4406b3d95f110d6e2f3f,

title = "Band selection for biomedical hyperspectral data studies using genetic algorithms",

abstract = "Hyperspectral imaging coupled with microscopy has been introduced for biomedicine science recently. As hyperspectral data cube include a great deal of single-band images, an adaptive genetic algorithm is presented to solve the best band combination problem for hyperspectral biomedical image studies in this paper. Simulation result demonstrates the effect of this algorithm to the hyperspectral image of leukemia blood cell.",

keywords = "Band combination, Genetic algorithm, Hyperspectral imaging",

author = "Chunni Dai and Qingli Li and Jingao Liu",

year = "2009",

doi = "10.1109/ICBBE.2009.5162435",

language = "英语",

isbn = "9781424429028",

series = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009",

booktitle = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009",

note = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 ; Conference date: 11-06-2009 Through 13-06-2009",

}

Dai, C, Li, Q & Liu, J 2009, Band selection for biomedical hyperspectral data studies using genetic algorithms. in 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009., 5162435, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, Beijing, China, 11/06/09. https://doi.org/10.1109/ICBBE.2009.5162435

Band selection for biomedical hyperspectral data studies using genetic algorithms. / Dai, Chunni; Li, Qingli; Liu, Jingao.
3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009. 2009. 5162435 (3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009).

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

TY - GEN

T1 - Band selection for biomedical hyperspectral data studies using genetic algorithms

AU - Dai, Chunni

AU - Li, Qingli

AU - Liu, Jingao

PY - 2009

Y1 - 2009

N2 - Hyperspectral imaging coupled with microscopy has been introduced for biomedicine science recently. As hyperspectral data cube include a great deal of single-band images, an adaptive genetic algorithm is presented to solve the best band combination problem for hyperspectral biomedical image studies in this paper. Simulation result demonstrates the effect of this algorithm to the hyperspectral image of leukemia blood cell.

AB - Hyperspectral imaging coupled with microscopy has been introduced for biomedicine science recently. As hyperspectral data cube include a great deal of single-band images, an adaptive genetic algorithm is presented to solve the best band combination problem for hyperspectral biomedical image studies in this paper. Simulation result demonstrates the effect of this algorithm to the hyperspectral image of leukemia blood cell.

KW - Band combination

KW - Genetic algorithm

KW - Hyperspectral imaging

UR - https://www.scopus.com/pages/publications/72749101443

U2 - 10.1109/ICBBE.2009.5162435

DO - 10.1109/ICBBE.2009.5162435

M3 - 会议稿件

AN - SCOPUS:72749101443

SN - 9781424429028

T3 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

BT - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Y2 - 11 June 2009 through 13 June 2009

ER -

Band selection for biomedical hyperspectral data studies using genetic algorithms

Abstract

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Keywords

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