1
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Devi S, Bakshi S, Sahoo MN. Effect of situational and instrumental distortions on the classification of brain MR images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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2
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Enhanced statistical nearest neighbors with steerable pyramid transform for Gaussian noise removal in a color image. EVOLUTIONARY INTELLIGENCE 2022. [DOI: 10.1007/s12065-021-00627-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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3
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He J, Gao P, Zheng X, Zhou Y, He H. Denoising 3D magnetic resonance images based on weighted tensor nuclear norm minimization using balanced nonlocal patch tensors. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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4
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Peng Y, Su X, Hu L, Wang Q, Ouyang R, Sun A, Guo C, Yao X, Zhang Y, Wang L, Zhong Y. Feasibility of Three-Dimensional Balanced Steady-State Free Precession Cine Magnetic Resonance Imaging Combined with an Image Denoising Technique to Evaluate Cardiac Function in Children with Repaired Tetralogy of Fallot. Korean J Radiol 2021; 22:1525-1536. [PMID: 34448382 PMCID: PMC8390812 DOI: 10.3348/kjr.2020.0850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 03/06/2021] [Accepted: 03/13/2021] [Indexed: 11/18/2022] Open
Abstract
Objective To investigate the feasibility of cine three-dimensional (3D) balanced steady-state free precession (b-SSFP) imaging combined with a non-local means (NLM) algorithm for image denoising in evaluating cardiac function in children with repaired tetralogy of Fallot (rTOF). Materials and Methods Thirty-five patients with rTOF (mean age, 12 years; range, 7–18 years) were enrolled to undergo cardiac cine image acquisition, including two-dimensional (2D) b-SSFP, 3D b-SSFP, and 3D b-SSFP combined with NLM. End-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), and ejection fraction (EF) of the two ventricles were measured and indexed by body surface index. Acquisition time and image quality were recorded and compared among the three imaging sequences. Results 3D b-SSFP with denoising vs. 2D b-SSFP had high correlation coefficients for EDV, ESV, SV, and EF of the left (0.959–0.991; p < 0.001) as well as right (0.755–0.965; p < 0.001) ventricular metrics. The image acquisition time ± standard deviation (SD) was 25.1 ± 2.4 seconds for 3D b-SSFP compared with 277.6 ± 0.7 seconds for 2D b-SSFP, indicating a significantly shorter time with the 3D than the 2D sequence (p < 0.001). Image quality score was better with 3D b-SSFP combined with denoising than with 3D b-SSFP (mean ± SD, 3.8 ± 0.6 vs. 3.5 ± 0.6; p = 0.005). Signal-to-noise ratios for blood and myocardium as well as contrast between blood and myocardium were higher for 3D b-SSFP combined with denoising than for 3D b-SSFP (p < 0.05 for all but septal myocardium). Conclusion The 3D b-SSFP sequence can significantly reduce acquisition time compared to the 2D b-SSFP sequence for cine imaging in the evaluation of ventricular function in children with rTOF, and its quality can be further improved by combining it with an NLM denoising method.
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Affiliation(s)
- YaFeng Peng
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - XinYu Su
- University of Shanghai for Science and Technology, Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, Shanghai, China
| | - LiWei Hu
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qian Wang
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - RongZhen Ouyang
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - AiMin Sun
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Guo
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - XiaoFen Yao
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yong Zhang
- MR Research, GE Healthcare, Shanghai, China
| | - LiJia Wang
- University of Shanghai for Science and Technology, Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, Shanghai, China
| | - YuMin Zhong
- Diagnostic Imaging Center of Shanghai Children's Medical Center Affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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5
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A novel method for removing Rician noise from MRI based on variational mode decomposition. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102737] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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6
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Mishro PK, Agrawal S, Panda R, Abraham A. A Survey on State-of-the-art Denoising Techniques for Brain Magnetic Resonance Images. IEEE Rev Biomed Eng 2021; 15:184-199. [PMID: 33513109 DOI: 10.1109/rbme.2021.3055556] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The accuracy of the magnetic resonance (MR) image diagnosis depends on the quality of the image, which degrades mainly due to noise and artifacts. The noise is introduced because of erroneous imaging environment or distortion in the transmission system. Therefore, denoising methods play an important role in enhancing the image quality. However, a tradeoff between denoising and preserving the structural details is required. Most of the existing surveys are conducted on a specific MR image modality or on limited denoising schemes. In this context, a comprehensive review on different MR image denoising techniques is inevitable. This survey suggests a new direction in categorizing the MR image denoising techniques. The categorization of the different image models used in medical image processing serves as the basis of our classification. This study includes recent improvements on deep learning-based denoising methods alongwith important traditional MR image denoising methods. The major challenges and their scope of improvement are also discussed. Further, many more evaluation indices are considered for a fair comparison. An elaborate discussion on selecting appropriate method and evaluation metric as per the kind of data is presented. This study may encourage researchers for further work in this domain.
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7
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Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10207028] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The non-local means (NLM) noise reduction algorithm is well known as an excellent technique for removing noise from a magnetic resonance (MR) image to improve the diagnostic accuracy. In this study, we undertook a systematic review to determine the effectiveness of the NLM noise reduction algorithm in MR imaging. A systematic literature search was conducted of three databases of publications dating from January 2000 to March 2020; of the 82 publications reviewed, 25 were included in this study. The subjects were categorized into four major frameworks and analyzed for each research result. Research in NLM noise reduction for MR images has been increasing worldwide; however, it was found to have slightly decreased since 2016. It was found that the NLM technique was most frequently used on brain images taken using the general MR imaging technique; these were most frequently performed during simultaneous real and simulated experimental studies. In particular, comparison parameters were frequently used to evaluate the effectiveness of the algorithm on MR images. The ultimate goal is to provide an accurate method for the diagnosis of disease, and our conclusion is that the NLM noise reduction algorithm is a promising method of achieving this goal.
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8
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Cem Birbiri U, Hamidinekoo A, Grall A, Malcolm P, Zwiggelaar R. Investigating the Performance of Generative Adversarial Networks for Prostate Tissue Detection and Segmentation. J Imaging 2020; 6:jimaging6090083. [PMID: 34460740 PMCID: PMC8321056 DOI: 10.3390/jimaging6090083] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 08/14/2020] [Accepted: 08/18/2020] [Indexed: 12/24/2022] Open
Abstract
The manual delineation of region of interest (RoI) in 3D magnetic resonance imaging (MRI) of the prostate is time-consuming and subjective. Correct identification of prostate tissue is helpful to define a precise RoI to be used in CAD systems in clinical practice during diagnostic imaging, radiotherapy and monitoring the progress of disease. Conditional GAN (cGAN), cycleGAN and U-Net models and their performances were studied for the detection and segmentation of prostate tissue in 3D multi-parametric MRI scans. These models were trained and evaluated on MRI data from 40 patients with biopsy-proven prostate cancer. Due to the limited amount of available training data, three augmentation schemes were proposed to artificially increase the training samples. These models were tested on a clinical dataset annotated for this study and on a public dataset (PROMISE12). The cGAN model outperformed the U-Net and cycleGAN predictions owing to the inclusion of paired image supervision. Based on our quantitative results, cGAN gained a Dice score of 0.78 and 0.75 on the private and the PROMISE12 public datasets, respectively.
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Affiliation(s)
- Ufuk Cem Birbiri
- Department of Computer Engineering, Middle East Technical University, Ankara 06800, Turkey;
| | - Azam Hamidinekoo
- Division of Molecular Pathology, Institute of Cancer Research (ICR), London SM2 5NG, UK;
| | | | - Paul Malcolm
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK;
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
- Correspondence:
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9
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Tripathi PC, Bag S. CNN-DMRI: A Convolutional Neural Network for Denoising of Magnetic Resonance Images. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.03.036] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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10
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Chen G, Dong B, Zhang Y, Lin W, Yap PT. Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:2838-2848. [PMID: 31071025 PMCID: PMC8325050 DOI: 10.1109/tmi.2019.2915629] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naïvely can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both x -space (spatial domain) and q -space (wavevector domain). Specifically, we first encode the q -space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naïvely applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, U.S.A. D. Shen is also with the Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yong Zhang
- Vancouver Research Center, Huawei, Burnaby, Canada
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11
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Bui AT, Im JK, Apley DW, Runger GC. Projection-free kernel principal component analysis for denoising. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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12
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Chen G, Wu Y, Shen D, Yap PT. Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space. Med Image Anal 2019; 53:79-94. [PMID: 30703580 PMCID: PMC6397790 DOI: 10.1016/j.media.2019.01.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 12/25/2018] [Accepted: 01/14/2019] [Indexed: 10/27/2022]
Abstract
Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.
| | - Yafeng Wu
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA.
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13
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PG K, Joseph J, Jayaraman S. A customized nonlocal restoration schemes with adaptive strength of smoothening for magnetic resonance images. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.12.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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14
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Chen G, Zhang J, Zhang Y, Dong B, Shen D, Yap PT. Multi-channel framelet denoising of diffusion-weighted images. PLoS One 2019; 14:e0211621. [PMID: 30726257 PMCID: PMC6364918 DOI: 10.1371/journal.pone.0211621] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 01/17/2019] [Indexed: 11/18/2022] Open
Abstract
Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
| | - Jian Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
- School of Information and Electrical Engineering, Hunan University of Science & Technology, Xiangtan, China
| | - Yong Zhang
- Vancouver Research Center, Huawei, Burnaby, Canada
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina, Chapel Hill, United States of America
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15
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16
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Chen G, Dong B, Zhang Y, Lin W, Shen D, Yap PT. Angular Upsampling in Infant Diffusion MRI Using Neighborhood Matching in x- q Space. Front Neuroinform 2018; 12:57. [PMID: 30245622 PMCID: PMC6137306 DOI: 10.3389/fninf.2018.00057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2018] [Accepted: 08/16/2018] [Indexed: 01/02/2023] Open
Abstract
Diffusion MRI requires sufficient coverage of the diffusion wavevector space, also known as the q-space, to adequately capture the pattern of water diffusion in various directions and scales. As a result, the acquisition time can be prohibitive for individuals who are unable to stay still in the scanner for an extensive period of time, such as infants. To address this problem, in this paper we harness non-local self-similar information in the x-q space of diffusion MRI data for q-space upsampling. Specifically, we first perform neighborhood matching to establish the relationships of signals in x-q space. The signal relationships are then used to regularize an ill-posed inverse problem related to the estimation of high angular resolution diffusion MRI data from its low-resolution counterpart. Our framework allows information from curved white matter structures to be used for effective regularization of the otherwise ill-posed problem. Extensive evaluations using synthetic and infant diffusion MRI data demonstrate the effectiveness of our method. Compared with the widely adopted interpolation methods using spherical radial basis functions and spherical harmonics, our method is able to produce high angular resolution diffusion MRI data with greater quality, both qualitatively and quantitatively.
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Affiliation(s)
- Geng Chen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yong Zhang
- Vancouver Research Center, Huawei Technologies Canada, Burnaby, BC, Canada
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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17
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Kim DG, Shamsi ZH. Enhanced residual noise estimation of low rank approximation for image denoising. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.063] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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18
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Chen G, Dong B, Zhang Y, Shen D, Yap PT. Neighborhood Matching for Curved Domains with Application to Denoising in Diffusion MRI. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2017; 10433:629-637. [PMID: 29577115 PMCID: PMC5860684 DOI: 10.1007/978-3-319-66182-7_72] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
In this paper, we introduce a strategy for performing neighborhood matching on general non-Euclidean and non-flat domains. Essentially, this involves representing the domain as a graph and then extending the concept of convolution from regular grids to graphs. Acknowledging the fact that convolutions are features of local neighborhoods, neighborhood matching is carried out using the outcome of multiple convolutions at multiple scales. All these concepts are encapsulated in a sound mathematical framework, called graph framelet transforms (GFTs), which allows signals residing on non-flat domains to be decomposed according to multiple frequency subbands for rich characterization of signal patterns. We apply GFTs to the problem of denoising of diffusion MRI data, which can reside on domains defined in very different ways, such as on a shell, on multiple shells, or on a Cartesian grid. Our non-local formulation of the problem allows information of diffusion signal profiles of drastically different orientations to be borrowed for effective denoising.
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Affiliation(s)
- Geng Chen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
| | - Bin Dong
- Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Yong Zhang
- Colin Artificial Intelligence Lab, Richmond, Canada
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, USA
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19
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Yang Z, Shen D, Yap PT. Image mosaicking using SURF features of line segments. PLoS One 2017; 12:e0173627. [PMID: 28296919 PMCID: PMC5351852 DOI: 10.1371/journal.pone.0173627] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 02/22/2017] [Indexed: 11/18/2022] Open
Abstract
In this paper, we present a novel image mosaicking method that is based on Speeded-Up Robust Features (SURF) of line segments, aiming to achieve robustness to incident scaling, rotation, change in illumination, and significant affine distortion between images in a panoramic series. Our method involves 1) using a SURF detection operator to locate feature points; 2) rough matching using SURF features of directed line segments constructed via the feature points; and 3) eliminating incorrectly matched pairs using RANSAC (RANdom SAmple Consensus). Experimental results confirm that our method results in high-quality panoramic mosaics that are superior to state-of-the-art methods.
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Affiliation(s)
- Zhanlong Yang
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, Shaanxi, China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, United States of America
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, United States of America
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, United States of America
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Chen G, Wu Y, Shen D, Yap PT. XQ-NLM: Denoising Diffusion MRI Data via x-q Space Non-Local Patch Matching. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2016; 9902:587-595. [PMID: 28090604 DOI: 10.1007/978-3-319-46726-9_68] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Noise is a major issue influencing quantitative analysis in diffusion MRI. The effects of noise can be reduced by repeated acquisitions, but this leads to long acquisition times that can be unrealistic in clinical settings. For this reason, post-acquisition denoising methods have been widely used to improve SNR. Among existing methods, non-local means (NLM) has been shown to produce good image quality with edge preservation. However, currently the application of NLM to diffusion MRI has been mostly focused on the spatial space (i.e., the x-space), despite the fact that diffusion data live in a combined space consisting of the x-space and the q-space (i.e., the space of wavevectors). In this paper, we propose to extend NLM to both x-space and q-space. We show how patch-matching, as required in NLM, can be performed concurrently in x-q space with the help of azimuthal equidistant projection and rotation invariant features. Extensive experiments on both synthetic and real data confirm that the proposed x-q space NLM (XQ-NLM) outperforms the classic NLM.
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Affiliation(s)
- Geng Chen
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China; Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A
| | - Yafeng Wu
- Data Processing Center, Northwestern Polytechnical University, Xi'an, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A
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