Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation
In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introdu...
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| Published in | IEEE transactions on medical imaging Vol. 32; no. 11; pp. 2034 - 2049 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.11.2013
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods. |
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| AbstractList | In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods. In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods. |
| Author | Kuo, Anthony N. Leyuan Fang Shutao Li Farsiu, Sina Qing Nie Toth, Cynthia A. McNabb, Ryan P. Izatt, Joseph A. |
| Author_xml | – sequence: 1 surname: Leyuan Fang fullname: Leyuan Fang email: fangleyuan@gmail.com organization: Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China – sequence: 2 surname: Shutao Li fullname: Shutao Li email: shutao_li@hnu.edu.cn organization: Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China – sequence: 3 givenname: Ryan P. surname: McNabb fullname: McNabb, Ryan P. email: rpm10@duke.edu organization: Dept. of Biomed. Eng., Duke Univ., Durham, NC, USA – sequence: 4 surname: Qing Nie fullname: Qing Nie email: qing.nie@duke.edu organization: Dept. of Ophthalmology, Duke Univ. Med. Center, Durham, NC, USA – sequence: 5 givenname: Anthony N. surname: Kuo fullname: Kuo, Anthony N. email: anthony.kuo@duke.edu organization: Dept. of Ophthalmology, Duke Univ. Med. Center, Durham, NC, USA – sequence: 6 givenname: Cynthia A. surname: Toth fullname: Toth, Cynthia A. email: cynthia.toth@duke.edu organization: Dept. of Ophthalmology, Duke Univ. Med. Center, Durham, NC, USA – sequence: 7 givenname: Joseph A. surname: Izatt fullname: Izatt, Joseph A. email: jizatt@duke.edu organization: Dept. of Ophthalmology, Duke Univ. Med. Center, Durham, NC, USA – sequence: 8 givenname: Sina surname: Farsiu fullname: Farsiu, Sina email: sina.farsiu@duke.edu organization: Dept. of Ophthalmology, Duke Univ. Med. Center, Durham, NC, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23846467$$D View this record in MEDLINE/PubMed |
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| Snippet | In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our... In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our... |
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| SubjectTerms | Algorithms Animals Dictionaries Fast retina scanning Humans image enhancement Image Processing, Computer-Assisted - methods Image reconstruction Image resolution Imaging Interpolation Macular Degeneration - pathology Mice Noise reduction Optic Nerve - anatomy & histology Optical Coherence Tomography Reconstruction Representations Retina - anatomy & histology Retina - pathology simultaneous denoising and interpolation sparse representation Sparsity Tomography Tomography, Optical Coherence - methods Training |
| Title | Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation |
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