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Journal articles on the topic 'Quantitative image analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Smolej, Vito. "Quantitative Image Analysis." Microscopy and Microanalysis 9, S02 (2003): 748–49. http://dx.doi.org/10.1017/s1431927603443742.

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2

Ewing, Robert P., and Robert Horton. "Quantitative Color Image Analysis of Agronomic Images." Agronomy Journal 91, no. 1 (1999): 148–53. http://dx.doi.org/10.2134/agronj1999.00021962009100010023x.

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3

TAKAMATSU, TETSUROU, TADAHISA KITAMURA, and SETSUYA FUJITA. "Quantitative fluorescence image analysis." Acta Histochemica et Cytochemica 19, no. 1 (1986): 61–71. http://dx.doi.org/10.1267/ahc.19.61.

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4

Khan, Muhammad Aqeel Ahmad, and Ulrich Kohlenbach. "Quantitative image recovery theorems." Nonlinear Analysis: Theory, Methods & Applications 106 (September 2014): 138–50. http://dx.doi.org/10.1016/j.na.2014.04.015.

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5

VOORT, H. T. M., and K. C. STRASTERS. "Restoration of confocal images for quantitative image analysis." Journal of Microscopy 178, no. 2 (1995): 165–81. http://dx.doi.org/10.1111/j.1365-2818.1995.tb03593.x.

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6

Murthy, Chaitanya R., Bo Gao, Andrea R. Tao, and Gaurav Arya. "Automated quantitative image analysis of nanoparticle assembly." Nanoscale 7, no. 21 (2015): 9793–805. http://dx.doi.org/10.1039/c5nr00809c.

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7

Fernandes-Santos, Caroline, Vanessa Souza-Mello, Tatiane da Silva Faria, and Carlos Alberto Mandarim-de-Lacerda. "Quantitative Morphology Update: Image Analysis." International Journal of Morphology 31, no. 1 (2013): 23–30. http://dx.doi.org/10.4067/s0717-95022013000100003.

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8

Pantanowitz, Liron. "Quantitative image analysis for immunohistochemistry." Pathology 52 (February 2020): S11. http://dx.doi.org/10.1016/j.pathol.2020.01.080.

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9

Sandau, Konrad. "Quantitative Image Analysis in Darmstadt." Imaging & Microscopy 9, no. 3 (2007): 20. http://dx.doi.org/10.1002/imic.200790168.

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10

Oberholzer, Martin, Marc �streicher, Heinz Christen, and Marcel Br�hlmann. "Methods in quantitative image analysis." Histochemistry and Cell Biology 105, no. 5 (1996): 333–55. http://dx.doi.org/10.1007/bf01463655.

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11

Shpigler, B. "Image and structure quantitative analysis." Ultramicroscopy 19, no. 4 (1986): 397–98. http://dx.doi.org/10.1016/0304-3991(86)90126-9.

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12

Zhang, X., and V. K. Berry. "Quantitative image analysis of polymer blends." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 920–21. http://dx.doi.org/10.1017/s0424820100172334.

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Quantitative image analysis is important in understanding the role of microstructure in polymer blend properties, as revealed by the TEM. This paper presents an example of the application of the image analysis method to the study of structure/property relationship of an acrylonitrile-butadiene-styrene (ABS) polymer.ABS is a rubber-toughened two-phase polymer blend. As shown in Figure 1, the microstructure of ABSconsists of small rubber particles embedded in a styrene-acrylonitrile copolymer (SAN) matrix. The morphologies of these blends, which depend on the polymerization conditions, play a ke

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13

Kim, Gyuho, Jung Gon Kim, Kitaek Kang, and Woo Sik Yoo. "Image-Based Quantitative Analysis of Foxing Stains on Old Printed Paper Documents." Heritage 2, no. 3 (2019): 2665–77. http://dx.doi.org/10.3390/heritage2030164.

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We studied the feasibility of image-based quantitative analysis of foxing stains on collections of old (16th–20th century) European books stored in the Rare Book Library of the Seoul National University in Korea. We were able to quantitatively determine the foxing affected areas on books from their photographs using a newly developed image processing software (PicMan) including cultural property characterization applications, specifically. Dimensional and color analysis of photographs were successfully done quantitatively. Histograms of RGB (red, green, blue) pixels of photographs clearly show

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14

Nadarzinski, K., O. Kienzle, and F. Ernst. "Analysis of Grain-Boundary Structures by Quantitative Hrtem." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 326–27. http://dx.doi.org/10.1017/s042482010016409x.

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HRTEM (high-resolution transmission electron microscopy) constitutes a powerful technique to investigate the atomistic structure of grain boundaries. However, interpreting HRTEM images of grain boundaries in terms of “projected structure” may lead to errors: Near a boundary, the contrast patterns of atom columns may differ from the corresponding patterns in regions of unfaulted crystal. Even worse, dynamic diffraction and lens aberrations may displace the contrast patterns against the actual (projected) positions of the columns. Nevertheless, it is possible to interpret such images safely by c

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15

Pasqualin, Côme, François Gannier, Claire O. Malécot, Pierre Bredeloux, and Véronique Maupoil. "Automatic quantitative analysis of t-tubule organization in cardiac myocytes using ImageJ." American Journal of Physiology-Cell Physiology 308, no. 3 (2015): C237—C245. http://dx.doi.org/10.1152/ajpcell.00259.2014.

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The transverse tubule system in mammalian striated muscle is highly organized and contributes to optimal and homogeneous contraction. Diverse pathologies such as heart failure and atrial fibrillation include disorganization of t-tubules and contractile dysfunction. Few tools are available for the quantification of the organization of the t-tubule system. We developed a plugin for the ImageJ/Fiji image analysis platform developed by the National Institutes of Health. This plugin (TTorg) analyzes raw confocal microscopy images. Analysis options include the whole image, specific regions of the im

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16

Rungroadsri, Jinda, Wadcharawadee Limsakul, Worawit Wongniramaikul, and Aree Choodum. "Rapid Semi-Quantitative Analysis of Formaldehyde in Food by Digital Image Colorimetry." International Journal of Chemical Engineering and Applications 8, no. 4 (2017): 294–98. http://dx.doi.org/10.18178/ijcea.2017.8.4.673.

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17

Ciaccio, Edward J. "Quantitative image analysis of celiac disease." World Journal of Gastroenterology 21, no. 9 (2015): 2577. http://dx.doi.org/10.3748/wjg.v21.i9.2577.

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18

Bassel, George W. "Accuracy in Quantitative 3D Image Analysis." Plant Cell 27, no. 4 (2015): 950–53. http://dx.doi.org/10.1105/tpc.114.135061.

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19

Pons, Marie-Noëlle, and Hervé Vivier. "Crystallization monitoring by quantitative image analysis." Analytica Chimica Acta 238 (1990): 243–49. http://dx.doi.org/10.1016/s0003-2670(00)80543-7.

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20

Ploem, J. S., A. M. J. van Driel-Kulker, L. Goyarts-Veldstra, J. J. Ploem-Zaaijer, N. P. Verwoerd, and M. van der Zwan. "Image analysis combined with quantitative cytochemistry." Histochemistry 84, no. 4-6 (1986): 549–55. http://dx.doi.org/10.1007/bf00482990.

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21

Grillon, F., D. Fayeulle, and M. Jeandin. "Quantitative image analysis of electrophoretic coatings." Journal of Materials Science Letters 11, no. 5 (1992): 272–75. http://dx.doi.org/10.1007/bf00729410.

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22

Wendt, R. E., E. S. Delpassand,, and D. A. Podoloff. "SEMI-AUTOMATED QUANTITATIVE THYROID IMAGE ANALYSIS." CLINICAL NUCLEAR MEDICINE 22, no. 3 (1997): 204. http://dx.doi.org/10.1097/00003072-199703000-00047.

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23

Garcia Garrido, Marina, Susanne C. Beck, Regine Mühlfriedel, Sylvie Julien, Ulrich Schraermeyer, and Mathias W. Seeliger. "Towards a Quantitative OCT Image Analysis." PLoS ONE 9, no. 6 (2014): e100080. http://dx.doi.org/10.1371/journal.pone.0100080.

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24

Chadda, V. K., D. G. Joshi, S. N. Murthy, et al. "Image analysis system for quantitative metallography." Bulletin of Materials Science 8, no. 2 (1986): 231–37. http://dx.doi.org/10.1007/bf02744188.

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25

Rečnik, Aleksander, Günter Möbus, and Sašo Šturm. "Quantitative HAADF-STEM image analysis using IMAGE-WARP processing." Microscopy and Microanalysis 9, S03 (2003): 52–53. http://dx.doi.org/10.1017/s1431927603012145.

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26

Lu, Yi, Chenyang Huang, Jia Wang, and Peng Shang. "An Improved Quantitative Analysis Method for Plant Cortical Microtubules." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/637183.

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The arrangement of plant cortical microtubules can reflect the physiological state of cells. However, little attention has been paid to the image quantitative analysis of plant cortical microtubules so far. In this paper, Bidimensional Empirical Mode Decomposition (BEMD) algorithm was applied in the image preprocessing of the original microtubule image. And then Intrinsic Mode Function 1 (IMF1) image obtained by decomposition was selected to do the texture analysis based on Grey-Level Cooccurrence Matrix (GLCM) algorithm. Meanwhile, in order to further verify its reliability, the proposed text

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27

Fukui, K., and K. Kakeda. "Quantitative karyotyping of barley chromosomes by image analysis methods." Genome 33, no. 3 (1990): 450–58. http://dx.doi.org/10.1139/g90-067.

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Barley chromosomes were analyzed by the chromosome image analyzing system, CHIAS. Stained and unstained diploid metaphase spreads were automatically scanned and their locations on the glass slide were detected. With stained chromosomes, detection efficiency of good metaphase plates exceeded on average 90%. Three image parameters, length, area, and density volume, of each chromosome were defined and measured for 250 haploid chromosome plates. Of these parameters, total length and the arm ratio of the length were the most informative for chromosome identification. A quantitative idiogram of the

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28

Wang, Xiaobin. "Quantitative Analysis of Multiple Food Additives in Wheat Flour by Raman Hyperspectral Imaging." Journal of Biobased Materials and Bioenergy 15, no. 5 (2021): 656–62. http://dx.doi.org/10.1166/jbmb.2021.2101.

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Raman hyperspectral imaging can obtain both the internal Raman signals and the external image information of the sample simultaneously. This study investigated the quantitatively analysis of multiple food additives in wheat flour by using this technology. Raman hyperspectral images of wheat flour containing the three additives, L-ascorbate acid (LAA), azodicarbonamide (ADC) and benzoyl peroxide (BPO), were collected. Raman signals in Raman hyperspectral images were preprocessed by smoothing and baseline correction methods to obtain the corrected image. Chemical images were created to visually

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29

Murphy, TF. "Quantitative Analysis of Fracture Surfaces Using Image Analysis." Microscopy and Microanalysis 14, S2 (2008): 44–45. http://dx.doi.org/10.1017/s143192760808255x.

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30

Turner, J. N., W. hain, D. H. Szarowski, et al. "Three Dimensional Light Microscopy: Imaging & Corrections for Quantitative Analysis." Microscopy and Microanalysis 5, S2 (1999): 522–23. http://dx.doi.org/10.1017/s1431927600015932.

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There are several forms of three-dimensional (3-D) light microscopy but all utilize the principle of optical section recording, i.e. the 3-D image is a sequence of two-dimensional (2-D) images called optical sections. The optical sections are particular focal planes formed within the thick specimen and usually correspond to the conventional image projections recorded in a light microscope, referred to as x,y projections. The optical sections are recorded for a sequence of focus- or z-positions. This “stack” of 2-D images is the data set for the 3-D image. If quantitative analysis is to be perf

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31

Deserno, T. M., H. P. Meinzer, T. Tolxdorff, and H. Handels. "Image Analysis and Modeling in Medical Image Computing." Methods of Information in Medicine 51, no. 05 (2012): 395–97. http://dx.doi.org/10.1055/s-0038-1627047.

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Summary Background: Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. Objectives: In the focus theme, recent developments and advances in the field of modeling and model-ba

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32

Song, Peng Ran, and Chang Ming Wang. "Study for Quantitative Analysis of Loess Microstructure Influence." Advanced Materials Research 594-597 (November 2012): 522–26. http://dx.doi.org/10.4028/www.scientific.net/amr.594-597.522.

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Microstructure is a important index of soil physical, mechanical and engineering properties, SEM images and computer image processing technology make the soil microstructure research developing rapidly in recent years, but the researches on the influence factors and important degree are rare. Process the images form scanning electron microscopy test with the image processing toolbox of MATLAB. Fractal dimensions, porosities and pore size distributions are calculated in different analyzing windows, thresholds and magnifications. The results show that:1) As the results of the experiment influenc

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33

Ain, Khusnul, Lina Choridah, Deddy Kurniadi, Agah D. Garnadi, Utriweni Mukhayyar, and Nurhuda Hendra Setyawan. "QUANTITATIVE ANALYSIS OF ELECTRICAL CURRENT EFFECT ON MAGNETIC RESONANCE IMAGE TISSUE INTENSITY." Jurnal Teknologi 85, no. 2 (2023): 141–48. http://dx.doi.org/10.11113/jurnalteknologi.v85.18871.

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Recent studies in magnetic resonance imaging (MRI) aim to improve image quality while reducing scan time. Electrical current injection in the form of magnetic resonance electrical impedance tomography (MREIT) is believed to be affecting image quality and scan time thus can improve the possibility of becoming a non-chemical contrast agent in MRI. This study will observe and analyze the effect of electrical current injection on a phantom object to determine whether there is a different tissue image intensity. A thigh of lamb was used as a biological tissue phantom. The scan was performed on both

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34

Hu, Qi Zhi, Jing Xia Wang, and Gao Liang Tao. "Quantitative Analysis of Soft Soil Microstructure in Unloading Levels." Applied Mechanics and Materials 401-403 (September 2013): 1529–33. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1529.

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Quantitative analysis of soft soil microstructure in unloading levels are made by using scanning electron microscope (SEM) images, IPP and PS of image technology ,which includes image segmentation, pore size measuring and counting, three dimensional simulation of soft soil microstructure, etc. The results indicate that, with the increase of unloading grade, pore number and area of big aperture are in a sharp increase, the corresponding porosity also in ascension, so the deformation of the soil is mainly due to the change of pore; compared with the apparent 3d images of soil under the transvers

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35

Thomas, Laurent S. V., Franz Schaefer, and Jochen Gehrig. "Fiji plugins for qualitative image annotations: routine analysis and application to image classification." F1000Research 9 (October 15, 2020): 1248. http://dx.doi.org/10.12688/f1000research.26872.1.

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Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkb

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36

Barnaby, Roger J. "Quantitative Image Analysis For Geologic Core Description." Journal of Sedimentary Research 87, no. 5 (2017): 460–85. http://dx.doi.org/10.2110/jsr.2017.25.

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37

Favara, Blaise E., Ann Steele, John H. Grant, and Peter Steele. "Adrenal Cytomegaly: Quantitative Assessment by Image Analysis." Pediatric Pathology 11, no. 4 (1991): 521–36. http://dx.doi.org/10.3109/15513819109064788.

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38

Werner, A. M., and D. A. Lange. "Quantitative Image Analysis of Masonry Mortar Microstructure." Journal of Computing in Civil Engineering 13, no. 2 (1999): 110–15. http://dx.doi.org/10.1061/(asce)0887-3801(1999)13:2(110).

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39

Zaidi, H. "BASIC CONCEPTS OF QUANTITATIVE DYNAMIC IMAGE ANALYSIS." Radiotherapy and Oncology 92 (August 2009): S112. http://dx.doi.org/10.1016/s0167-8140(12)72883-9.

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40

Wang, De-feng. "Quantitative medical image analysis of musculoskeletal system." Journal of Orthopaedic Translation 2, no. 4 (2014): 191. http://dx.doi.org/10.1016/j.jot.2014.07.105.

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41

Ramot, Y. "Quantitative image analysis for hereditary hair disorders." British Journal of Dermatology 176, no. 1 (2017): 10–11. http://dx.doi.org/10.1111/bjd.15103.

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42

Parry, William L., and George P. Hemstreet. "Cancer Detection by Quantitative Fluorescence Image Analysis." Journal of Urology 139, no. 2 (1988): 270–74. http://dx.doi.org/10.1016/s0022-5347(17)42384-6.

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43

Karlsson, M. G., Â. Davidsson, and H. B. Heliquist. "Quantitative Computerized Image Analysis of Immunostained Lymphocytes." Pathology - Research and Practice 190, no. 8 (1994): 799–807. http://dx.doi.org/10.1016/s0344-0338(11)80428-0.

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44

Wied, George L., Peter H. Bartels, Marluce Bibbo, and Harvey E. Dytch. "Image analysis in quantitative cytopathology and histopathology." Human Pathology 20, no. 6 (1989): 549–71. http://dx.doi.org/10.1016/0046-8177(89)90245-1.

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45

Fernandez-Gonzalez, Rodrigo, Mary Helen Barcellos-Hoff, and Carlos Ortiz-de-Sol�rzano. "Quantitative Image Analysis in Mammary Gland Biology." Journal of Mammary Gland Biology and Neoplasia 9, no. 4 (2004): 343–59. http://dx.doi.org/10.1007/s10911-004-1405-9.

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46

Russ, John C. "Computer-assisted image analysis in quantitative fractography." JOM 42, no. 10 (1990): 16–19. http://dx.doi.org/10.1007/bf03220405.

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47

Asa, SylviaL, Zoya Volynskaya, Ozgur Mete, Sara Pakbaz, and Doaa Al-Ghamdi. "Ki67 quantitative interpretation: Insights using image analysis." Journal of Pathology Informatics 10, no. 1 (2019): 8. http://dx.doi.org/10.4103/jpi.jpi_76_18.

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48

Macdonald, I. F., P. Kaufmann, and F. A. L. Dullien. "Quantitative image analysis of finite porous media." Journal of Microscopy 144, no. 3 (1986): 277–96. http://dx.doi.org/10.1111/j.1365-2818.1986.tb02807.x.

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49

Macdonald, I. F., P. Kaufmann, and F. A. L. Dullien. "Quantitative image analysis of finite porous media." Journal of Microscopy 144, no. 3 (1986): 297–316. http://dx.doi.org/10.1111/j.1365-2818.1986.tb02808.x.

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50

Ross, S., R. Newton, Y. ‐M Zhou, et al. "Quantitative image analysis of birefringent biological material." Journal of Microscopy 187, no. 1 (1997): 62–67. http://dx.doi.org/10.1046/j.1365-2818.1997.2160776.x.

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