Image Processing, Computer-Assisted

"Image Processing, Computer-Assisted" is a descriptor in the National Library of Medicine's controlled vocabulary thesaurus, MeSH (Medical Subject Headings). Descriptors are arranged in a hierarchical structure, which enables searching at various levels of specificity.

MeSH information

Definition | Details | More General Concepts | Related Concepts | More Specific Concepts

A technique of inputting two-dimensional images into a computer and then enhancing or analyzing the imagery into a form that is more useful to the human observer.

Descriptor ID	D007091
MeSH Number(s)	L01.224.308
Concept/Terms	Image Processing, Computer-Assisted Image Processing, Computer-Assisted Image Processing, Computer Assisted Computer-Assisted Image Processing Computer Assisted Image Processing Image Reconstruction Image Reconstruction Image Reconstructions Reconstruction, Image Reconstructions, Image Image Analysis, Computer-Assisted Image Analysis, Computer-Assisted Image Analysis, Computer Assisted Computer-Assisted Image Analysis Computer Assisted Image Analysis Analysis, Computer-Assisted Image Computer-Assisted Image Analyses Image Analyses, Computer-Assisted

Below are MeSH descriptors whose meaning is more general than "Image Processing, Computer-Assisted".

Information Science [L]
Information Science [L01]
Computing Methodologies [L01.224]
Image Processing, Computer-Assisted [L01.224.308]

Below are MeSH descriptors whose meaning is related to "Image Processing, Computer-Assisted".

Below are MeSH descriptors whose meaning is more specific than "Image Processing, Computer-Assisted".

publications

Timeline | Most Recent

This graph shows the total number of publications written about "Image Processing, Computer-Assisted" by people in this website by year, and whether "Image Processing, Computer-Assisted" was a major or minor topic of these publications.

Bar chart showing 436 publications over 31 distinct years, with a maximum of 26 publications in 2011

To see the data from this visualization as text, click here.

Year	Major Topic	Minor Topic	Total
1993	1	1	2
1994	0	6	6
1995	1	5	6
1996	4	5	9
1997	2	3	5
1998	5	8	13
1999	4	7	11
2000	2	7	9
2001	2	4	6
2002	2	10	12
2003	6	12	18
2004	7	18	25
2005	4	21	25
2006	5	18	23
2007	4	20	24
2008	3	16	19
2009	4	14	18
2010	5	11	16
2011	10	16	26
2012	3	10	13
2013	5	17	22
2014	5	18	23
2015	7	13	20
2016	3	9	12
2017	3	9	12
2018	6	16	22
2019	6	5	11
2020	5	6	11
2021	5	8	13
2022	1	2	3
2023	0	1	1

Below are the most recent publications written about "Image Processing, Computer-Assisted" by people in Profiles.

Liu Z, Wolfe S, Yu Z, Laforest R, Mhlanga JC, Fraum TJ, Itani M, Dehdashti F, Siegel BA, Jha AK. Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images. Phys Med Biol. 2023 03 21; 68(7).

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Sun J, Zhang Q, Du Y, Zhang D, Pretorius PH, King MA, Mok GSP. Dual gating myocardial perfusion SPECT denoising using a conditional generative adversarial network. Med Phys. 2022 Aug; 49(8):5093-5106.

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Tseng HW, Karellas A, Vedantham S. Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm. Phys Med Biol. 2022 04 07; 67(8).

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Berg M, Holroyd N, Walsh C, West H, Walker-Samuel S, Shipley R. Challenges and opportunities of integrating imaging and mathematical modelling to interrogate biological processes. Int J Biochem Cell Biol. 2022 05; 146:106195.

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Pretorius PH, King MA. Data-driven respiratory signal estimation from temporally finely sampled projection data in conventional cardiac perfusion SPECT imaging. Med Phys. 2022 Jan; 49(1):282-294.

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Rigano A, Ehmsen S, ?zt?rk SU, Ryan J, Balashov A, Hammer M, Kirli K, Boehm U, Brown CM, Bellve K, Chambers JJ, Cosolo A, Coleman RA, Faklaris O, Fogarty KE, Guilbert T, Hamacher AB, Itano MS, Keeley DP, Kunis S, Lacoste J, Laude A, Ma WY, Marcello M, Montero-Llopis P, Nelson G, Nitschke R, Pimentel JA, Weidtkamp-Peters S, Park PJ, Alver BH, Grunwald D, Strambio-De-Castillia C. Micro-Meta App: an interactive tool for collecting microscopy metadata based on community specifications. Nat Methods. 2021 12; 18(12):1489-1495.

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Sarkans U, Chiu W, Collinson L, Darrow MC, Ellenberg J, Grunwald D, H?rich? JK, Iudin A, Martins GG, Meehan T, Narayan K, Patwardhan A, Russell MRG, Saibil HR, Strambio-De-Castillia C, Swedlow JR, Tischer C, Uhlmann V, Verkade P, Barlow M, Bayraktar O, Birney E, Catavitello C, Cawthorne C, Wagner-Conrad S, Duke E, Paul-Gilloteaux P, Gustin E, Harkiolaki M, Kankaanp?? P, Lemberger T, McEntyre J, Moore J, Nicholls AW, Onami S, Parkinson H, Parsons M, Romanchikova M, Sofroniew N, Swoger J, Utz N, Voortman LM, Wong F, Zhang P, Kleywegt GJ, Brazma A. REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology. Nat Methods. 2021 12; 18(12):1418-1422.

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Pirruccello JP, Chaffin MD, Chou EL, Fleming SJ, Lin H, Nekoui M, Khurshid S, Friedman SF, Bick AG, Arduini A, Weng LC, Choi SH, Akkad AD, Batra P, Tucker NR, Hall AW, Roselli C, Benjamin EJ, Vellarikkal SK, Gupta RM, Stegmann CM, Juric D, Stone JR, Vasan RS, Ho JE, Hoffmann U, Lubitz SA, Philippakis AA, Lindsay ME, Ellinor PT. Deep learning enables genetic analysis of the human thoracic aorta. Nat Genet. 2022 01; 54(1):40-51.

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Saranathan M, Iglehart C, Monti M, Tourdias T, Rutt B. In vivo high-resolution structural MRI-based atlas of human thalamic nuclei. Sci Data. 2021 10 28; 8(1):275.

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Umapathy L, Keerthivasan MB, Zahr NM, Bilgin A, Saranathan M. Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI. Neuroinformatics. 2022 Jul; 20(3):651-664.

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