An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology
Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for...
Ausführliche Beschreibung
Autor*in: |
Rao, Karishma [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2022 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
---|
Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Springer US, 1982, 42(2022), 2 vom: 15. Sept., Seite 1034-1062 |
---|---|
Übergeordnetes Werk: |
volume:42 ; year:2022 ; number:2 ; day:15 ; month:09 ; pages:1034-1062 |
Links: |
---|
DOI / URN: |
10.1007/s00034-022-02163-8 |
---|
Katalog-ID: |
OLC2133715061 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2133715061 | ||
003 | DE-627 | ||
005 | 20230506153033.0 | ||
007 | tu | ||
008 | 230506s2022 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s00034-022-02163-8 |2 doi | |
035 | |a (DE-627)OLC2133715061 | ||
035 | |a (DE-He213)s00034-022-02163-8-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 600 |q VZ |
100 | 1 | |a Rao, Karishma |e verfasserin |0 (orcid)0000-0002-2437-8927 |4 aut | |
245 | 1 | 0 | |a An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
520 | |a Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. | ||
650 | 4 | |a Top-hat transform | |
650 | 4 | |a CT image | |
650 | 4 | |a Image enhancement | |
650 | 4 | |a Dual-tree complex wavelet transform | |
700 | 1 | |a Bansal, Manu |4 aut | |
700 | 1 | |a Kaur, Gagandeep |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Circuits, systems and signal processing |d Springer US, 1982 |g 42(2022), 2 vom: 15. Sept., Seite 1034-1062 |w (DE-627)130312134 |w (DE-600)588684-3 |w (DE-576)015889939 |x 0278-081X |7 nnns |
773 | 1 | 8 | |g volume:42 |g year:2022 |g number:2 |g day:15 |g month:09 |g pages:1034-1062 |
856 | 4 | 1 | |u https://doi.org/10.1007/s00034-022-02163-8 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-TEC | ||
912 | |a GBV_ILN_2244 | ||
951 | |a AR | ||
952 | |d 42 |j 2022 |e 2 |b 15 |c 09 |h 1034-1062 |
author_variant |
k r kr m b mb g k gk |
---|---|
matchkey_str |
article:0278081X:2022----::nfetvcmdclmgehneetytmaeodctn |
hierarchy_sort_str |
2022 |
publishDate |
2022 |
allfields |
10.1007/s00034-022-02163-8 doi (DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p DE-627 ger DE-627 rakwb eng 600 VZ Rao, Karishma verfasserin (orcid)0000-0002-2437-8927 aut An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Springer US, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 42 2022 2 15 09 1034-1062 |
spelling |
10.1007/s00034-022-02163-8 doi (DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p DE-627 ger DE-627 rakwb eng 600 VZ Rao, Karishma verfasserin (orcid)0000-0002-2437-8927 aut An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Springer US, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 42 2022 2 15 09 1034-1062 |
allfields_unstemmed |
10.1007/s00034-022-02163-8 doi (DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p DE-627 ger DE-627 rakwb eng 600 VZ Rao, Karishma verfasserin (orcid)0000-0002-2437-8927 aut An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Springer US, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 42 2022 2 15 09 1034-1062 |
allfieldsGer |
10.1007/s00034-022-02163-8 doi (DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p DE-627 ger DE-627 rakwb eng 600 VZ Rao, Karishma verfasserin (orcid)0000-0002-2437-8927 aut An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Springer US, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 42 2022 2 15 09 1034-1062 |
allfieldsSound |
10.1007/s00034-022-02163-8 doi (DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p DE-627 ger DE-627 rakwb eng 600 VZ Rao, Karishma verfasserin (orcid)0000-0002-2437-8927 aut An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Springer US, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 0278-081X nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 AR 42 2022 2 15 09 1034-1062 |
language |
English |
source |
Enthalten in Circuits, systems and signal processing 42(2022), 2 vom: 15. Sept., Seite 1034-1062 volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 |
sourceStr |
Enthalten in Circuits, systems and signal processing 42(2022), 2 vom: 15. Sept., Seite 1034-1062 volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform |
dewey-raw |
600 |
isfreeaccess_bool |
false |
container_title |
Circuits, systems and signal processing |
authorswithroles_txt_mv |
Rao, Karishma @@aut@@ Bansal, Manu @@aut@@ Kaur, Gagandeep @@aut@@ |
publishDateDaySort_date |
2022-09-15T00:00:00Z |
hierarchy_top_id |
130312134 |
dewey-sort |
3600 |
id |
OLC2133715061 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2133715061</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506153033.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230506s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00034-022-02163-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2133715061</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00034-022-02163-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rao, Karishma</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2437-8927</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Top-hat transform</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CT image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image enhancement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dual-tree complex wavelet transform</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bansal, Manu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kaur, Gagandeep</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Circuits, systems and signal processing</subfield><subfield code="d">Springer US, 1982</subfield><subfield code="g">42(2022), 2 vom: 15. Sept., Seite 1034-1062</subfield><subfield code="w">(DE-627)130312134</subfield><subfield code="w">(DE-600)588684-3</subfield><subfield code="w">(DE-576)015889939</subfield><subfield code="x">0278-081X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:42</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">day:15</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:1034-1062</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00034-022-02163-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">42</subfield><subfield code="j">2022</subfield><subfield code="e">2</subfield><subfield code="b">15</subfield><subfield code="c">09</subfield><subfield code="h">1034-1062</subfield></datafield></record></collection>
|
author |
Rao, Karishma |
spellingShingle |
Rao, Karishma ddc 600 misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
authorStr |
Rao, Karishma |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)130312134 |
format |
Article |
dewey-ones |
600 - Technology |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0278-081X |
topic_title |
600 VZ An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform |
topic |
ddc 600 misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform |
topic_unstemmed |
ddc 600 misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform |
topic_browse |
ddc 600 misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Circuits, systems and signal processing |
hierarchy_parent_id |
130312134 |
dewey-tens |
600 - Technology |
hierarchy_top_title |
Circuits, systems and signal processing |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)130312134 (DE-600)588684-3 (DE-576)015889939 |
title |
An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
ctrlnum |
(DE-627)OLC2133715061 (DE-He213)s00034-022-02163-8-p |
title_full |
An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
author_sort |
Rao, Karishma |
journal |
Circuits, systems and signal processing |
journalStr |
Circuits, systems and signal processing |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
600 - Technology |
recordtype |
marc |
publishDateSort |
2022 |
contenttype_str_mv |
txt |
container_start_page |
1034 |
author_browse |
Rao, Karishma Bansal, Manu Kaur, Gagandeep |
container_volume |
42 |
class |
600 VZ |
format_se |
Aufsätze |
author-letter |
Rao, Karishma |
doi_str_mv |
10.1007/s00034-022-02163-8 |
normlink |
(ORCID)0000-0002-2437-8927 |
normlink_prefix_str_mv |
(orcid)0000-0002-2437-8927 |
dewey-full |
600 |
title_sort |
an effective ct medical image enhancement system based on dt-cwt and adaptable morphology |
title_auth |
An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
abstract |
Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_2244 |
container_issue |
2 |
title_short |
An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
url |
https://doi.org/10.1007/s00034-022-02163-8 |
remote_bool |
false |
author2 |
Bansal, Manu Kaur, Gagandeep |
author2Str |
Bansal, Manu Kaur, Gagandeep |
ppnlink |
130312134 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s00034-022-02163-8 |
up_date |
2024-07-03T21:08:37.457Z |
_version_ |
1803593625063391232 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2133715061</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230506153033.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230506s2022 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00034-022-02163-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2133715061</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00034-022-02163-8-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rao, Karishma</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2437-8927</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Enhancing the quality of diagnostic images and preserving their original features is crucial for early detection and further analysis. In non-contrast CT imaging, a noisy and low contrast CT image can give inadequate information for the visual analysis of affected regions. A new method for enhancing non-contrast CT images with dual-tree complex wavelet transform (DT-CWT) and adaptable morphology is presented in this paper. Input CT images are inserted into the DT-CWT system, resulting in low- and high-frequency subbands. On high-frequency subbands, denoising is performed using the wavelet-related shearlet transform method, which results in enhanced high-frequency sub-images. An adaptive morphology top-hat transform technique is used with DCT-based local enhancement to enhance the low-frequency sub-images. The improved low and high-frequency components are then recombined to form the enhanced CT image using inverse DT-CWT. In order to estimate the success of the proposed system, experiments and validations are carried out on a diverse collection of CT images taken from publicly accessible databases. An extensive quantitative analysis demonstrates that the proposed method outperforms existing image enhancement techniques in terms of peak signal-to-noise ratio, entropy, contrast ratio, and measure of enhancement. In the proposed algorithm, the contrast is enhanced while maintaining the brightness and natural characteristics of the CT image. The proposed approach produces CT images of higher quality, which can be useful for detecting and diagnosing illnesses.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Top-hat transform</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CT image</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image enhancement</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dual-tree complex wavelet transform</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bansal, Manu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kaur, Gagandeep</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Circuits, systems and signal processing</subfield><subfield code="d">Springer US, 1982</subfield><subfield code="g">42(2022), 2 vom: 15. Sept., Seite 1034-1062</subfield><subfield code="w">(DE-627)130312134</subfield><subfield code="w">(DE-600)588684-3</subfield><subfield code="w">(DE-576)015889939</subfield><subfield code="x">0278-081X</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:42</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:2</subfield><subfield code="g">day:15</subfield><subfield code="g">month:09</subfield><subfield code="g">pages:1034-1062</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00034-022-02163-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2244</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">42</subfield><subfield code="j">2022</subfield><subfield code="e">2</subfield><subfield code="b">15</subfield><subfield code="c">09</subfield><subfield code="h">1034-1062</subfield></datafield></record></collection>
|
score |
7.400402 |