Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging
Vascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image microvessels by applying tissue clutter filtering methods on the spatio-temporal data obtained from plane-wave imaging. However, the resultant vessel images suffer fro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/19/2/245 |
| _version_ | 1828395122917638144 |
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| author | Saba Adabi Siavash Ghavami Mostafa Fatemi Azra Alizad |
| author_facet | Saba Adabi Siavash Ghavami Mostafa Fatemi Azra Alizad |
| author_sort | Saba Adabi |
| collection | DOAJ |
| description | Vascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image microvessels by applying tissue clutter filtering methods on the spatio-temporal data obtained from plane-wave imaging. However, the resultant vessel images suffer from background noise that degrades image quality and restricts vessel visibilities. In this paper, we addressed microvessel visualization and the associated noise problem in the power Doppler images with the goal of achieving enhanced vessel-background separation. We proposed a combination of patch-based non-local mean filtering and top-hat morphological filtering to improve vessel outline and background noise suppression. We tested the proposed method on a flow phantom, as well as in vivo breast lesions, thyroid nodules, and pathologic liver from human subjects. The proposed non-local-based framework provided a remarkable gain of more than 15 dB, on average, in terms of contrast-to-noise and signal-to-noise ratios. In addition to improving visualization of microvessels, the proposed method provided high quality images suitable for microvessel morphology quantification that may be used for diagnostic applications. |
| first_indexed | 2024-12-10T08:07:16Z |
| format | Article |
| id | doaj.art-feb1cf84b10741a5a57ea91394175244 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T08:07:16Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-feb1cf84b10741a5a57ea913941752442022-12-22T01:56:39ZengMDPI AGSensors1424-82202019-01-0119224510.3390/s19020245s19020245Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel ImagingSaba Adabi0Siavash Ghavami1Mostafa Fatemi2Azra Alizad3Department of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN 55905, USADepartment of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN 55905, USADepartment of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine & Science, Rochester, MN 55905, USADepartment of Radiology, Mayo Clinic College of Medicine & Science, Rochester, MN 55905, USAVascular networks can provide invaluable information about tumor angiogenesis. Ultrafast Doppler imaging enables ultrasound to image microvessels by applying tissue clutter filtering methods on the spatio-temporal data obtained from plane-wave imaging. However, the resultant vessel images suffer from background noise that degrades image quality and restricts vessel visibilities. In this paper, we addressed microvessel visualization and the associated noise problem in the power Doppler images with the goal of achieving enhanced vessel-background separation. We proposed a combination of patch-based non-local mean filtering and top-hat morphological filtering to improve vessel outline and background noise suppression. We tested the proposed method on a flow phantom, as well as in vivo breast lesions, thyroid nodules, and pathologic liver from human subjects. The proposed non-local-based framework provided a remarkable gain of more than 15 dB, on average, in terms of contrast-to-noise and signal-to-noise ratios. In addition to improving visualization of microvessels, the proposed method provided high quality images suitable for microvessel morphology quantification that may be used for diagnostic applications.http://www.mdpi.com/1424-8220/19/2/245medical imagingDoppler microvessel imagingnoise suppressionnon-local based denoisingsingular value decomposition |
| spellingShingle | Saba Adabi Siavash Ghavami Mostafa Fatemi Azra Alizad Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging Sensors medical imaging Doppler microvessel imaging noise suppression non-local based denoising singular value decomposition |
| title | Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging |
| title_full | Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging |
| title_fullStr | Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging |
| title_full_unstemmed | Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging |
| title_short | Non-Local Based Denoising Framework for In Vivo Contrast-Free Ultrasound Microvessel Imaging |
| title_sort | non local based denoising framework for in vivo contrast free ultrasound microvessel imaging |
| topic | medical imaging Doppler microvessel imaging noise suppression non-local based denoising singular value decomposition |
| url | http://www.mdpi.com/1424-8220/19/2/245 |
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