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] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| 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. |
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| Übergeordnetes Werk: |
Enthalten in: Circuits, systems and signal processing - Springer US, 1982, 42(2022), 2 vom: 15. Sept., Seite 1034-1062 |
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| Übergeordnetes Werk: |
volume:42 ; year:2022 ; number:2 ; day:15 ; month:09 ; pages:1034-1062 |
| Links: |
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| DOI / URN: |
10.1007/s00034-022-02163-8 |
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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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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 |
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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 |
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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 |
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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 |
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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 |
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An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
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An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
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Rao, Karishma |
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an effective ct medical image enhancement system based on dt-cwt and adaptable morphology |
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An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
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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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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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An Effective CT Medical Image Enhancement System Based on DT-CWT and Adaptable Morphology |
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