Texture based classification and segmentation of tissues using DT-CWT feature extraction methods

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Texture based classification and segmentation of tissues using DT-CWT feature extraction methods. / Aydogan, Dogu Baran; Hannula, Markus; Arola, Tuukka; Hyttinen, Jari; Dastidar, Prasun.

Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008. 2008. p. 614-619 4562069.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Aydogan, DB, Hannula, M, Arola, T, Hyttinen, J & Dastidar, P 2008, Texture based classification and segmentation of tissues using DT-CWT feature extraction methods. in Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008., 4562069, pp. 614-619, IEEE International Symposium on Computer-Based Medical Systems, Jyväskylä, Finland, 17/06/2008. https://doi.org/10.1109/CBMS.2008.46

APA

Aydogan, D. B., Hannula, M., Arola, T., Hyttinen, J., & Dastidar, P. (2008). Texture based classification and segmentation of tissues using DT-CWT feature extraction methods. In Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008 (pp. 614-619). [4562069] https://doi.org/10.1109/CBMS.2008.46

Vancouver

Aydogan DB, Hannula M, Arola T, Hyttinen J, Dastidar P. Texture based classification and segmentation of tissues using DT-CWT feature extraction methods. In Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008. 2008. p. 614-619. 4562069 https://doi.org/10.1109/CBMS.2008.46

Author

Aydogan, Dogu Baran ; Hannula, Markus ; Arola, Tuukka ; Hyttinen, Jari ; Dastidar, Prasun. / Texture based classification and segmentation of tissues using DT-CWT feature extraction methods. Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008. 2008. pp. 614-619

Bibtex - Download

@inproceedings{40b396c7afb04899ba0a25d0533473c5,
title = "Texture based classification and segmentation of tissues using DT-CWT feature extraction methods",
abstract = "In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.",
author = "Aydogan, {Dogu Baran} and Markus Hannula and Tuukka Arola and Jari Hyttinen and Prasun Dastidar",
year = "2008",
month = "9",
day = "22",
doi = "10.1109/CBMS.2008.46",
language = "English",
isbn = "9780769531656",
pages = "614--619",
booktitle = "Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008",

}

RIS - Download

TY - GEN

T1 - Texture based classification and segmentation of tissues using DT-CWT feature extraction methods

AU - Aydogan, Dogu Baran

AU - Hannula, Markus

AU - Arola, Tuukka

AU - Hyttinen, Jari

AU - Dastidar, Prasun

PY - 2008/9/22

Y1 - 2008/9/22

N2 - In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.

AB - In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.

UR - http://www.scopus.com/inward/record.url?scp=51849099089&partnerID=8YFLogxK

U2 - 10.1109/CBMS.2008.46

DO - 10.1109/CBMS.2008.46

M3 - Conference contribution

SN - 9780769531656

SP - 614

EP - 619

BT - Proceedings of the 21st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2008

ER -

ID: 29135672