Image guided filtering for improving photoacoustic tomographic image reconstruction
Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filterin...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Online Access: | https://hdl.handle.net/10356/82511 http://hdl.handle.net/10220/46742 |
| _version_ | 1811683103204179968 |
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| author | Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
| author2 | School of Chemical and Biomedical Engineering |
| author_facet | School of Chemical and Biomedical Engineering Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
| author_sort | Awasthi, Navchetan |
| collection | NTU |
| description | Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. |
| first_indexed | 2024-10-01T04:07:24Z |
| format | Journal Article |
| id | ntu-10356/82511 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:07:24Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/825112023-12-29T06:47:26Z Image guided filtering for improving photoacoustic tomographic image reconstruction Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. School of Chemical and Biomedical Engineering Photoacoustic Imaging Guided Image Filtering DRNTU::Engineering::Bioengineering Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them. Accepted version 2018-11-29T08:39:23Z 2019-12-06T14:57:03Z 2018-11-29T08:39:23Z 2019-12-06T14:57:03Z 2018 2018 Journal Article Awasthi, N., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2018). Image guided filtering for improving photoacoustic tomographic image reconstruction. Journal of Biomedical Optics, 23(09), 1-. doi:10.1117/1.JBO.23.9.091413 1083-3668 https://hdl.handle.net/10356/82511 http://hdl.handle.net/10220/46742 10.1117/1.JBO.23.9.091413 208396 208396 en Journal of Biomedical Optics © 2018 Society of Photo-optical Instrumentation Engineers (SPIE). This is the author created version of a work that has been peer reviewed and accepted for publication by Journal of Biomedical Optics, Society of Photo-optical Instrumentation Engineers (SPIE). It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1117/1.JBO.23.9.091413]. 67 p. application/pdf |
| spellingShingle | Photoacoustic Imaging Guided Image Filtering DRNTU::Engineering::Bioengineering Awasthi, Navchetan Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title | Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title_full | Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title_fullStr | Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title_full_unstemmed | Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title_short | Image guided filtering for improving photoacoustic tomographic image reconstruction |
| title_sort | image guided filtering for improving photoacoustic tomographic image reconstruction |
| topic | Photoacoustic Imaging Guided Image Filtering DRNTU::Engineering::Bioengineering |
| url | https://hdl.handle.net/10356/82511 http://hdl.handle.net/10220/46742 |
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