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Not picture-perfect.

Nature

[No authors listed]
PMID: 16495950
Nature. 2006 Feb 23;439(7079):891-2. doi: 10.1038/439891b.

No abstract available.

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Not picture-perfect. Nature. 2006;439(7079):891-2doi: 10.1038/439891b.
(2006). Not picture-perfect. Nature, 439(7079), 891-2. https://doi.org/10.1038/439891b
"Not picture-perfect." Nature vol. 439,7079 (2006): 891-2. doi: https://doi.org/10.1038/439891b
Not picture-perfect. Nature. 2006 Feb 23;439(7079):891-2. doi: 10.1038/439891b. PMID: 16495950.
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Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI.

NeuroImage

Cieslak M, Brennan T, Meiring W, Volz LJ, Greene C, Asturias A, Suri S, Grafton ST.
PMID: 29274744
Neuroimage. 2018 Apr 01;169:473-484. doi: 10.1016/j.neuroimage.2017.12.039. Epub 2017 Dec 22.

White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI signal is used to estimate the orientation of axon...

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Cieslak M, Brennan T, Meiring W, et al. Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI. Neuroimage. 2017;169:473-484doi: 10.1016/j.neuroimage.2017.12.039.
Cieslak, M., Brennan, T., Meiring, W., Volz, L. J., Greene, C., Asturias, A., Suri, S., & Grafton, S. T. (2018). Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI. NeuroImage, 169473-484. https://doi.org/10.1016/j.neuroimage.2017.12.039
Cieslak, Matthew, et al. "Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI." NeuroImage vol. 169 (2018): 473-484. doi: https://doi.org/10.1016/j.neuroimage.2017.12.039
Cieslak M, Brennan T, Meiring W, Volz LJ, Greene C, Asturias A, Suri S, Grafton ST. Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI. Neuroimage. 2018 Apr 01;169:473-484. doi: 10.1016/j.neuroimage.2017.12.039. Epub 2017 Dec 22. PMID: 29274744.
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Equipment, standardization, and applications of image processing.

American journal of clinical pathology

Wells WA, Rainer RO, Memoli VA.
PMID: 8422017
Am J Clin Pathol. 1993 Jan;99(1):48-56. doi: 10.1093/ajcp/99.1.48.

No abstract available.

Cite
Wells WA, Rainer RO, Memoli VA. Equipment, standardization, and applications of image processing. Am J Clin Pathol. 1993;99(1):48-56doi: 10.1093/ajcp/99.1.48.
Wells, W. A., Rainer, R. O., & Memoli, V. A. (1993). Equipment, standardization, and applications of image processing. American journal of clinical pathology, 99(1), 48-56. https://doi.org/10.1093/ajcp/99.1.48
Wells, W A, et al. "Equipment, standardization, and applications of image processing." American journal of clinical pathology vol. 99,1 (1993): 48-56. doi: https://doi.org/10.1093/ajcp/99.1.48
Wells WA, Rainer RO, Memoli VA. Equipment, standardization, and applications of image processing. Am J Clin Pathol. 1993 Jan;99(1):48-56. doi: 10.1093/ajcp/99.1.48. PMID: 8422017.
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Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework.

NeuroImage

Kindalova P, Kosmidis I, Nichols TE.
PMID: 33895308
Neuroimage. 2021 Aug 01;236:118090. doi: 10.1016/j.neuroimage.2021.118090. Epub 2021 Apr 22.

OBJECTIVES: White matter lesions are a very common finding on MRI in older adults and their presence increases the risk of stroke and dementia. Accurate and computationally efficient modelling methods are necessary to map the association of lesion incidence...

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Kindalova P, Kosmidis I, Nichols TE. Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework. Neuroimage. 2021;236:118090doi: 10.1016/j.neuroimage.2021.118090.
Kindalova, P., Kosmidis, I., & Nichols, T. E. (2021). Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework. NeuroImage, 236118090. https://doi.org/10.1016/j.neuroimage.2021.118090
Kindalova, Petya, et al. "Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework." NeuroImage vol. 236 (2021): 118090. doi: https://doi.org/10.1016/j.neuroimage.2021.118090
Kindalova P, Kosmidis I, Nichols TE. Voxel-wise and spatial modelling of binary lesion masks: Comparison of methods with a realistic simulation framework. Neuroimage. 2021 Aug 01;236:118090. doi: 10.1016/j.neuroimage.2021.118090. Epub 2021 Apr 22. PMID: 33895308.
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Deep learning on image denoising: An overview.

Neural networks : the official journal of the International Neural Network Society

Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin CW.
PMID: 32829002
Neural Netw. 2020 Nov;131:251-275. doi: 10.1016/j.neunet.2020.07.025. Epub 2020 Aug 06.

Deep learning techniques have received much attention in the area of image denoising. However, there are substantial differences in the various types of deep learning methods dealing with image denoising. Specifically, discriminative learning based on deep learning can ably...

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Tian C, Fei L, Zheng W, et al. Deep learning on image denoising: An overview. Neural Netw. 2020;131:251-275doi: 10.1016/j.neunet.2020.07.025.
Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C. W. (2020). Deep learning on image denoising: An overview. Neural networks : the official journal of the International Neural Network Society, 131251-275. https://doi.org/10.1016/j.neunet.2020.07.025
Tian, Chunwei, et al. "Deep learning on image denoising: An overview." Neural networks : the official journal of the International Neural Network Society vol. 131 (2020): 251-275. doi: https://doi.org/10.1016/j.neunet.2020.07.025
Tian C, Fei L, Zheng W, Xu Y, Zuo W, Lin CW. Deep learning on image denoising: An overview. Neural Netw. 2020 Nov;131:251-275. doi: 10.1016/j.neunet.2020.07.025. Epub 2020 Aug 06. PMID: 32829002.
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Error in radiology-where are we now?.

The British journal of radiology

Maskell G.
PMID: 30457880
Br J Radiol. 2019 Mar;92(1095):20180845. doi: 10.1259/bjr.20180845. Epub 2018 Nov 28.

Error is inherent in radiological practice. Our awareness of the extent of this and the reasons behind it has increased in recent times. Our next step must be the development of a shared understanding with our patients of the...

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Maskell G. Error in radiology-where are we now?. Br J Radiol. 2018;92(1095):20180845doi: 10.1259/bjr.20180845.
Maskell, G. (2019). Error in radiology-where are we now?. The British journal of radiology, 92(1095), 20180845. https://doi.org/10.1259/bjr.20180845
Maskell, Giles. "Error in radiology-where are we now?." The British journal of radiology vol. 92,1095 (2019): 20180845. doi: https://doi.org/10.1259/bjr.20180845
Maskell G. Error in radiology-where are we now?. Br J Radiol. 2019 Mar;92(1095):20180845. doi: 10.1259/bjr.20180845. Epub 2018 Nov 28. PMID: 30457880; PMCID: PMC6540865.
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A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment.

Journal of the American Medical Informatics Association : JAMIA

Goede PA, Lauman JR, Cochella C, Katzman GL, Morton DA, Albertine KH.
PMID: 14527971
J Am Med Inform Assoc. 2004 Jan-Feb;11(1):29-41. doi: 10.1197/jamia.M1247. Epub 2003 Oct 05.

Use of digital medical images has become common over the last several years, coincident with the release of inexpensive, mega-pixel quality digital cameras and the transition to digital radiology operation by hospitals. One problem that clinicians, medical educators, and...

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Goede PA, Lauman JR, Cochella C, et al. A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment. J Am Med Inform Assoc. 2003;11(1):29-41doi: 10.1197/jamia.M1247.
Goede, P. A., Lauman, J. R., Cochella, C., Katzman, G. L., Morton, D. A., & Albertine, K. H. (2004). A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment. Journal of the American Medical Informatics Association : JAMIA, 11(1), 29-41. https://doi.org/10.1197/jamia.M1247
Goede, Patricia A, et al. "A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment." Journal of the American Medical Informatics Association : JAMIA vol. 11,1 (2004): 29-41. doi: https://doi.org/10.1197/jamia.M1247
Goede PA, Lauman JR, Cochella C, Katzman GL, Morton DA, Albertine KH. A methodology and implementation for annotating digital images for context-appropriate use in an academic health care environment. J Am Med Inform Assoc. 2004 Jan-Feb;11(1):29-41. doi: 10.1197/jamia.M1247. Epub 2003 Oct 05. PMID: 14527971; PMCID: PMC305455.
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Unsupervised SVM-based gridding for DNA microarray images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Bariamis D, Maroulis D, Iakovidis DK.
PMID: 19879109
Comput Med Imaging Graph. 2010 Sep;34(6):418-25. doi: 10.1016/j.compmedimag.2009.09.005. Epub 2009 Oct 29.

This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray...

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Bariamis D, Maroulis D, Iakovidis DK. Unsupervised SVM-based gridding for DNA microarray images. Comput Med Imaging Graph. 2009;34(6):418-25doi: 10.1016/j.compmedimag.2009.09.005.
Bariamis, D., Maroulis, D., & Iakovidis, D. K. (2010). Unsupervised SVM-based gridding for DNA microarray images. Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 34(6), 418-25. https://doi.org/10.1016/j.compmedimag.2009.09.005
Bariamis, Dimitris, et al. "Unsupervised SVM-based gridding for DNA microarray images." Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society vol. 34,6 (2010): 418-25. doi: https://doi.org/10.1016/j.compmedimag.2009.09.005
Bariamis D, Maroulis D, Iakovidis DK. Unsupervised SVM-based gridding for DNA microarray images. Comput Med Imaging Graph. 2010 Sep;34(6):418-25. doi: 10.1016/j.compmedimag.2009.09.005. Epub 2009 Oct 29. PMID: 19879109.
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Progress in computer-generated three-dimensional reconstruction.

The Journal of pathology

Salisbury JR, Whimster WF.
PMID: 8133395
J Pathol. 1993 Jul;170(3):223-7. doi: 10.1002/path.1711700303.

No abstract available.

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Salisbury JR, Whimster WF. Progress in computer-generated three-dimensional reconstruction. J Pathol. 1993;170(3):223-7doi: 10.1002/path.1711700303.
Salisbury, J. R., & Whimster, W. F. (1993). Progress in computer-generated three-dimensional reconstruction. The Journal of pathology, 170(3), 223-7. https://doi.org/10.1002/path.1711700303
Salisbury, J R, and Whimster, W F. "Progress in computer-generated three-dimensional reconstruction." The Journal of pathology vol. 170,3 (1993): 223-7. doi: https://doi.org/10.1002/path.1711700303
Salisbury JR, Whimster WF. Progress in computer-generated three-dimensional reconstruction. J Pathol. 1993 Jul;170(3):223-7. doi: 10.1002/path.1711700303. PMID: 8133395.
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CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization.

Neural networks : the official journal of the International Neural Network Society

Cao J, Liu S, Liu H, Lu H.
PMID: 31884182
Neural Netw. 2020 Mar;123:217-233. doi: 10.1016/j.neunet.2019.12.010. Epub 2019 Dec 17.

Compressed sensing (CS) significantly accelerates magnetic resonance imaging (MRI) by allowing the exact reconstruction of image from highly undersampling k-space data. In this process, the high sparsity obtained by the learned dictionary and exploitation of correlation among patches are...

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Cao J, Liu S, Liu H, et al. CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization. Neural Netw. 2019;123:217-233doi: 10.1016/j.neunet.2019.12.010.
Cao, J., Liu, S., Liu, H., & Lu, H. (2020). CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization. Neural networks : the official journal of the International Neural Network Society, 123217-233. https://doi.org/10.1016/j.neunet.2019.12.010
Cao, Jianxin, et al. "CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization." Neural networks : the official journal of the International Neural Network Society vol. 123 (2020): 217-233. doi: https://doi.org/10.1016/j.neunet.2019.12.010
Cao J, Liu S, Liu H, Lu H. CS-MRI reconstruction based on analysis dictionary learning and manifold structure regularization. Neural Netw. 2020 Mar;123:217-233. doi: 10.1016/j.neunet.2019.12.010. Epub 2019 Dec 17. PMID: 31884182.
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Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression.

Neural networks : the official journal of the International Neural Network Society

Tang Y, Yang X, Wang N, Song B, Gao X.
PMID: 32018160
Neural Netw. 2020 Apr;124:223-232. doi: 10.1016/j.neunet.2020.01.012. Epub 2020 Jan 25.

Compared with face recognition, the performance of person re-identification (re-ID) is still far from practical application. Among various interferences, there are two factors seriously limiting the performance improvement, i.e., the feature discriminability determined by "external network effectiveness", and the...

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Tang Y, Yang X, Wang N, et al. Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression. Neural Netw. 2020;124:223-232doi: 10.1016/j.neunet.2020.01.012.
Tang, Y., Yang, X., Wang, N., Song, B., & Gao, X. (2020). Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression. Neural networks : the official journal of the International Neural Network Society, 124223-232. https://doi.org/10.1016/j.neunet.2020.01.012
Tang, Yingzhi, et al. "Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression." Neural networks : the official journal of the International Neural Network Society vol. 124 (2020): 223-232. doi: https://doi.org/10.1016/j.neunet.2020.01.012
Tang Y, Yang X, Wang N, Song B, Gao X. Person Re-Identification with Feature Pyramid Optimization and Gradual Background Suppression. Neural Netw. 2020 Apr;124:223-232. doi: 10.1016/j.neunet.2020.01.012. Epub 2020 Jan 25. PMID: 32018160.
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Evaluation of the effect of varying MPEG-2 compression ratios on digital coronary angiographic assessment of stenosis severity.

Journal of digital imaging

Okura Y, Matsumura Y, Hidaka K, Yokoyama H, Inada H, Harauchi H, Kou H, Inamura K.
PMID: 12415462
J Digit Imaging. 2002 Dec;15(4):210-5. doi: 10.1007/s10278-002-0013-8. Epub 2002 Nov 06.

The purpose of this study is to evaluate the influence of MPEG-2 compression scheme on coronary angiography and to search the highest compression ratio at which no significant effect to accuracy of assessment of stenosis severity occurs. Forty-Four digital...

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Okura Y, Matsumura Y, Hidaka K, et al. Evaluation of the effect of varying MPEG-2 compression ratios on digital coronary angiographic assessment of stenosis severity. J Digit Imaging. 2002;15(4):210-5doi: 10.1007/s10278-002-0013-8.
Okura, Y., Matsumura, Y., Hidaka, K., Yokoyama, H., Inada, H., Harauchi, H., Kou, H., & Inamura, K. (2002). Evaluation of the effect of varying MPEG-2 compression ratios on digital coronary angiographic assessment of stenosis severity. Journal of digital imaging, 15(4), 210-5. https://doi.org/10.1007/s10278-002-0013-8
Okura, Yasuhiko, et al. "Evaluation of the effect of varying MPEG-2 compression ratios on digital coronary angiographic assessment of stenosis severity." Journal of digital imaging vol. 15,4 (2002): 210-5. doi: https://doi.org/10.1007/s10278-002-0013-8
Okura Y, Matsumura Y, Hidaka K, Yokoyama H, Inada H, Harauchi H, Kou H, Inamura K. Evaluation of the effect of varying MPEG-2 compression ratios on digital coronary angiographic assessment of stenosis severity. J Digit Imaging. 2002 Dec;15(4):210-5. doi: 10.1007/s10278-002-0013-8. Epub 2002 Nov 06. PMID: 12415462; PMCID: PMC3611616.
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Showing 1 to 12 of 20 entries