Nonlinear Local Transformation Based Mammographic Image Enhancement

Cuiping Ding; Min Dong; Hongjuan Zhang; Yide Ma; Yaping Yan; Reyer Zwiggelaar

doi:10.1007/978-3-319-41546-8_22

Nonlinear Local Transformation Based Mammographic Image Enhancement

Cuiping Ding, Min Dong, Hongjuan Zhang, Yide Ma, Yaping Yan, Reyer Zwiggelaar

Adran Cyfrifiadureg

Lanzhou University

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

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Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).

Iaith wreiddiol	Saesneg
Teitl	Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings
Is-deitl	13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
Golygyddion	Kristina Lang, Anders Tingberg, Pontus Timberg
Cyhoeddwr	Springer Nature
Tudalennau	167-173
Nifer y tudalennau	7
ISBN (Electronig)	978-3-319-41546-8
ISBN (Argraffiad)	978-3-319-41545-1
Dynodwyr Gwrthrych Digidol (DOIs)	https://doi.org/10.1007/978-3-319-41546-8_22
Statws	Cyhoeddwyd - 17 Meh 2016

Cyfres gyhoeddiadau

Enw	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Cyfrol	9699
ISSN (Argraffiad)	0302-9743
ISSN (Electronig)	1611-3349

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Ding, C., Dong, M., Zhang, H., Ma, Y., Yan, Y., & Zwiggelaar, R. (2016). Nonlinear Local Transformation Based Mammographic Image Enhancement. Yn K. Lang, A. Tingberg, & P. Timberg (Gol.), Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings (tt. 167-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 9699). Springer Nature. https://doi.org/10.1007/978-3-319-41546-8_22

Ding, Cuiping ; Dong, Min ; Zhang, Hongjuan et al. / Nonlinear Local Transformation Based Mammographic Image Enhancement. Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Gol. / Kristina Lang ; Anders Tingberg ; Pontus Timberg. Springer Nature, 2016. tt. 167-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{71f44b242fba460ca6c7720a8c0927e2,

title = "Nonlinear Local Transformation Based Mammographic Image Enhancement",

abstract = "Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).",

keywords = "Local contrast enhancement, Mammography, Nonlinear transformation",

author = "Cuiping Ding and Min Dong and Hongjuan Zhang and Yide Ma and Yaping Yan and Reyer Zwiggelaar",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",

year = "2016",

month = jun,

day = "17",

doi = "10.1007/978-3-319-41546-8_22",

language = "English",

isbn = "978-3-319-41545-1",

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

publisher = "Springer Nature",

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Ding, C, Dong, M, Zhang, H, Ma, Y, Yan, Y & Zwiggelaar, R 2016, Nonlinear Local Transformation Based Mammographic Image Enhancement. yn K Lang, A Tingberg & P Timberg (gol.), Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), cyfrol. 9699, Springer Nature, tt. 167-173. https://doi.org/10.1007/978-3-319-41546-8_22

Nonlinear Local Transformation Based Mammographic Image Enhancement. / Ding, Cuiping; Dong, Min; Zhang, Hongjuan et al.
Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. gol. / Kristina Lang; Anders Tingberg; Pontus Timberg. Springer Nature, 2016. t. 167-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Cyfrol 9699).

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion Cynhadledd › Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)

TY - GEN

T1 - Nonlinear Local Transformation Based Mammographic Image Enhancement

AU - Ding, Cuiping

AU - Dong, Min

AU - Zhang, Hongjuan

AU - Ma, Yide

AU - Yan, Yaping

AU - Zwiggelaar, Reyer

PY - 2016/6/17

Y1 - 2016/6/17

N2 - Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).

AB - Mammography is one of the most effective techniques for early detection of breast cancer. The quality of the image may suffer from poor resolution or low contrast, which can effect the efficiency of radiologists. In order to improve the visual quality of mammograms, this paper introduces a new mammographic image enhancement algorithm. Firstly an intensity based nonlinear transformation is used for reducing the background tissue intensity, and secondly adaptive local contrast enhancement is realized based on local standard deviation and luminance information. The proposed method can obtain improved performance compared to alternative methods both covering objective and subjective aspects, based on 45 images. Experimental results demonstrate that the proposed algorithm can improve the contrast effectively and enhance lesion information (microcalcifications and/or masses).

KW - Local contrast enhancement

KW - Mammography

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SN - 978-3-319-41545-1

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

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BT - Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings

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A2 - Tingberg, Anders

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Ding C, Dong M, Zhang H, Ma Y, Yan Y, Zwiggelaar R. Nonlinear Local Transformation Based Mammographic Image Enhancement. Yn Lang K, Tingberg A, Timberg P, golygyddion, Breast Imaging - 13th International Workshop, IWDM 2016, Proceedings: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings. Springer Nature. 2016. t. 167-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2016 Meh 16. doi: 10.1007/978-3-319-41546-8_22

Nonlinear Local Transformation Based Mammographic Image Enhancement

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