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...
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Autor*in:	Rao, Karishma [verfasserIn] Bansal, Manu Kaur, Gagandeep

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Top-hat transform CT image Image enhancement Dual-tree complex wavelet transform

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 - Boston, Mass. : Birkhäuser, 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:	Volltext

DOI / URN:	10.1007/s00034-022-02163-8

Katalog-ID:	SPR049215450

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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.
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allfields	10.1007/s00034-022-02163-8 doi (DE-627)SPR049215450 (SPR)s00034-022-02163-8-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213 Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://dx.doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 2 15 09 1034-1062
spelling	10.1007/s00034-022-02163-8 doi (DE-627)SPR049215450 (SPR)s00034-022-02163-8-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213 Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://dx.doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 2 15 09 1034-1062
allfields_unstemmed	10.1007/s00034-022-02163-8 doi (DE-627)SPR049215450 (SPR)s00034-022-02163-8-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213 Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://dx.doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 2 15 09 1034-1062
allfieldsGer	10.1007/s00034-022-02163-8 doi (DE-627)SPR049215450 (SPR)s00034-022-02163-8-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213 Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://dx.doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 42 2022 2 15 09 1034-1062
allfieldsSound	10.1007/s00034-022-02163-8 doi (DE-627)SPR049215450 (SPR)s00034-022-02163-8-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213 Bansal, Manu aut Kaur, Gagandeep aut Enthalten in Circuits, systems and signal processing Boston, Mass. : Birkhäuser, 1982 42(2022), 2 vom: 15. Sept., Seite 1034-1062 (DE-627)351975470 (DE-600)2085136-4 1531-5878 nnns volume:42 year:2022 number:2 day:15 month:09 pages:1034-1062 https://dx.doi.org/10.1007/s00034-022-02163-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 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
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	351975470
id	SPR049215450
language_de	englisch
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author	Rao, Karishma
spellingShingle	Rao, Karishma 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
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topic_title	An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology Top-hat transform (dpeaa)DE-He213 CT image (dpeaa)DE-He213 Image enhancement (dpeaa)DE-He213 Dual-tree complex wavelet transform (dpeaa)DE-He213
topic	misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform
topic_unstemmed	misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform
topic_browse	misc Top-hat transform misc CT image misc Image enhancement misc Dual-tree complex wavelet transform
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title	An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology
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title_full	An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology
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journal	Circuits, systems and signal processing
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author_browse	Rao, Karishma Bansal, Manu Kaur, Gagandeep
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title_sort	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.
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title_short	An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology
url	https://dx.doi.org/10.1007/s00034-022-02163-8
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author2	Bansal, Manu Kaur, Gagandeep
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up_date	2024-07-03T23:52:46.419Z
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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><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">CT image</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image enhancement</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dual-tree complex wavelet transform</subfield><subfield code="7">(dpeaa)DE-He213</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">Boston, Mass. : Birkhäuser, 1982</subfield><subfield code="g">42(2022), 2 vom: 15. 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