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Готові списки джерел за темами / Semi-automatic analysis / Статті в журналах

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Статті в журналах з теми "Semi-automatic analysis"

Автор: Grafiati

Опубліковано: 10 березня 2023

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1

Touron, Patrick, Philippe Malaquin, Dominique Gardebas, and Jean-Pierre Nicolaı̈. "Semi-automatic analysis of fire debris." Forensic Science International 110, no. 1 (May 2000): 7–18. http://dx.doi.org/10.1016/s0379-0738(99)00220-0.

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2

KREKULE, I., L. KUBINOVA, J. KAMINSKI, and P. KAREN. "Computerized Systems for Semi-Automatic Stereological Analysis." Biomedizinische Technik/Biomedical Engineering 40, s2 (1995): 193–95. http://dx.doi.org/10.1515/bmte.1995.40.s2.193.

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3

Bartneck, Christoph, and Adrian Clark. "Semi-automatic color analysis for brand logos." Color Research & Application 40, no. 1 (November 4, 2013): 72–84. http://dx.doi.org/10.1002/col.21853.

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4

Michels, Mariane, Rodrigo Leitão de Assunção, Matheus Lima de Oliveira, Marcela Rodrigues Alves, Maria Augusta Portella Guedes Visconti, and Fábio Ribeiro Guedes. "Automatic cephalometric analysis: is it time to switch to a hands-free method?" Brazilian Journal of Oral Sciences 17 (December 6, 2018): e181095. http://dx.doi.org/10.20396/bjos.v17i0.8654178.

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Aim: To evaluate the reliability of the automatic cephalometric analysis in relation to the semi-automatic method. Methods: Fifty lateral cephalometric radiographs were selected and two dental surgeons performed the Steiner and Tweed analyses independently using the semi-automatic method on the Radiocef Studio 2® software suite (Radiomemory, Belo Horizonte, MG, Brazil), and the automatic method on the Kodak Dental Imaging Software (Carestream Health, Rochester, NY, USA). After thirty days, 30% of the sample was re-evaluated to assess intra-observer agreement. Ten angular and linear measurements of both analyses were selected, averaged for both observers and compared using Student's t-test with a significance level of 5% (α=0.05). Intra and inter-observer agreement were assessed through Intraclass Correlation Coefficient. Results: Intra-observer reproducibility was excellent for all measurements and inter-observer reproducibility was excellent for most of them. Significant differences (p<0.05) were found between automatic and semi-automatic methods for all measurements. Most of the measurements were significantly higher (p<0.05) with the automatic method. Conclusion: Semi-automatic cephalometric analysis can not be replaced with a completely automatic method.

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5

LIANG, Guang-Tai, Na MENG, Jin-Hui LI, Hao ZHONG, Lu ZHANG, and Qian-Xiang WANG. "A Semi-Automatic Extensible Static Defect Analysis Tool." Chinese Journal of Computers 34, no. 6 (September 6, 2011): 1114–25. http://dx.doi.org/10.3724/sp.j.1016.2011.01114.

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6

Neri, Paolo, Michela Fiaschi, and Giovanni Menchini. "Semi-Automatic Tool for Vitiligo Detection and Analysis." Journal of Imaging 6, no. 3 (March 24, 2020): 14. http://dx.doi.org/10.3390/jimaging6030014.

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Vitiligo vulgaris is an autoimmune disease which causes a strong reduction of the cells producing melanin, which is the main skin pigment. This results in the growth of white patches on patients’ skin, which are more or less visible, depending on the skin phototype. Precise, objective and fast detection of vitiligo patches would be crucial to produce statistically relevant data on huge populations, thus giving an insight on the disease. However, few methods are available in literature. In the present paper, a semi-automatic tool based on image processing to detect facial vitiligo patches is described. The tool requires pictures to be captured under black light illumination, which enhances patches contrast with respect to healthy skin. The user is only required to roughly define the regions of interest and set a global threshold, thus, no specific image-processing skills are required. An adaptive algorithm then automatically discerns between vitiligo and healthy skin pixels. The tools also allow for a statistical data interpretation by overlapping the detected patches of all patients on a face template through an occurrence map. Preliminary results obtained on a small population of 15 patients allowed us to assess the tool’s performance. Patch detection was checked by an experienced dermatologist, who confirmed the detection for all the studied patients, thus supporting the effectiveness of the proposed tool.

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7

Hunter, J. C., and M. W. Collins. "The semi-automatic analysis of compressible flow interferograms." Measurement Science and Technology 1, no. 3 (March 1, 1990): 238–46. http://dx.doi.org/10.1088/0957-0233/1/3/006.

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8

Sokhin, Timur, and Nikolay Butakov. "Semi-automatic sentiment analysis based on topic modeling." Procedia Computer Science 136 (2018): 284–92. http://dx.doi.org/10.1016/j.procs.2018.08.286.

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9

van Hoff, Arthur A., Tim Niblett, David G. Rowlands, Alan Flook, Peter I. Payne, and Sean McKee. "Semi-automatic analysis of two-dimensional electrophoretic gels." Knowledge-Based Systems 1, no. 5 (December 1988): 292–300. http://dx.doi.org/10.1016/0950-7051(88)90083-4.

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10

Krasić, Sonja, and Petar Pejić. "Comparative Analysis of Terrestrial Semi-automatic and Automatic Photogrammetry in 3D Modeling Process." Nexus Network Journal 16, no. 2 (May 13, 2014): 273–83. http://dx.doi.org/10.1007/s00004-014-0188-6.

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11

Buayai, Prawit, Kullapapruk Piewthongngam, Carson K. Leung, and Kanda Runapongsa Saikaew. "Semi-Automatic Pig Weight Estimation Using Digital Image Analysis." Applied Engineering in Agriculture 35, no. 4 (2019): 521–34. http://dx.doi.org/10.13031/aea.13084.

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Abstract. As pigs are usually sold by their weights, the weighing process plays an important role. Traditional approaches include manually weighing pigs in the actual farm environment, which can be a slow and stressful task due to the movement of the pigs in a crowded space. Moreover, the imprecision of the weights adds to this problem. Hence, an automatic approach is needed. However, existing automated procedures also suffer from issues like the imprecision caused by the pig movements, low light intensity, low ceiling in the farm environment for proper installation of a camera. To overcome these problems, this article presents a semi-automatic machine vision approach for pig weight estimation in an actual farm environment with a high number of pigs per pen, cloudy image background, and natural movement of the pigs. Commercial mechanical weight scales with radio-frequency identification (RFID) exist to deal with automatic pig weight monitoring for each stall. However, from an economic perspective, they are too expensive to employ in developing countries such as Thailand. The primary purpose of this study is to tackle the problem of cost. A low-cost closed-circuit television (CCTV) was exploited, and open-source software was adopted. Pig images were taken from the top view of the feeder. The developed software was equipped with camera calibration for the distortion elimination process, pig boundary detection for the feature extraction process, and artificial neural network for pig weight estimation. The experimental evaluation results showed the benefits and practicality of the proposed approach, which led to a low absolute mean error of 2.84%, a short model training time of approximately 5 seconds, and a short prediction time of 7.18 milliseconds. Keywords: Artificial intelligence, Image processing, Multilayer perceptron, Pig weight estimation system, Smart agriculture.

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12

Reyes-Aldasoro, Constantino Carlos, Kwun Ho Ngan, Ananda Ananda, Artur d’Avila Garcez, Andrew Appelboam, and Karen M. Knapp. "Geometric semi-automatic analysis of radiographs of Colles’ fractures." PLOS ONE 15, no. 9 (September 14, 2020): e0238926. http://dx.doi.org/10.1371/journal.pone.0238926.

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13

Syanda, A. M., G. Boe-Hansen, and N. Satake. "Semi-automatic morphometric analysis of normal boar sperm head." Animal Reproduction Science 169 (June 2016): 112. http://dx.doi.org/10.1016/j.anireprosci.2016.03.045.

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14

Philip-Joet, François F., Marc F. Rey, Andre A. DiCroco, Martine J. Reynaud-Gaubert, and Alain G. Arnaud. "Semi-automatic Analysis of Electroencephalogram in Sleep Apnea Syndromes." Chest 104, no. 2 (August 1993): 336–39. http://dx.doi.org/10.1378/chest.104.2.336.

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15

Schlickling, Marc, and Markus Pister. "Semi-automatic derivation of timing models for WCET analysis." ACM SIGPLAN Notices 45, no. 4 (April 13, 2010): 67–76. http://dx.doi.org/10.1145/1755951.1755899.

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16

Russo, A., P. Gualtieri, and N. Ricci. "A semi-automatic computerized analysis of tracks of ciliates." Experientia 44, no. 3 (March 1988): 277–80. http://dx.doi.org/10.1007/bf01941739.

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17

Romero-Ramirez, A., A. Grémare, M. Desmalades, and J. C. Duchêne. "Semi-automatic analysis and interpretation of sediment profile images." Environmental Modelling & Software 47 (September 2013): 42–54. http://dx.doi.org/10.1016/j.envsoft.2013.04.008.

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18

Beugher, Stijn De, Geert Brône, and Toon Goedemé. "A semi-automatic annotation tool for unobtrusive gesture analysis." Language Resources and Evaluation 52, no. 2 (November 7, 2017): 433–60. http://dx.doi.org/10.1007/s10579-017-9404-9.

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19

Hayman Oo, Khin, Azlin Nordin, Amelia Ritahani Ismail, and Suriani Sulaiman. "An Analysis of Ambiguity Detection Techniques for Software Requirements Specification (SRS)." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 501. http://dx.doi.org/10.14419/ijet.v7i2.29.13808.

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Анотація:

Ambiguity is the major problem in Software Requirements Specification (SRS) documents because most of the SRS documents are written in natural language and natural language is generally ambiguous. There are various types of techniques that have been used to detect ambiguity in SRS documents. Based on an analysis of the existing work, the ambiguity detection techniques can be categorized into three approaches: (1) manual approach, (2) semi-automatic approach using natural language processing, (3) semi-automatic approach using machine learning. Among them, one of the semi-automatic approaches that uses the Naïve Bayes (NB) text classification technique obtained high accuracy and performed effectively in detecting ambiguities in SRS.

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20

Mulchrone, Kieran F., Dave J. McCarthy, and Patrick A. Meere. "Mathematica code for image analysis, semi-automatic parameter extraction and strain analysis." Computers & Geosciences 61 (December 2013): 64–70. http://dx.doi.org/10.1016/j.cageo.2013.08.001.

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21

Spreafico, Christian, and Davide Russo. "A Semi-Automatic Methodology for Making FMEA Surveys." International Journal of Mathematical, Engineering and Management Sciences 6, no. 1 (October 29, 2020): 79–102. http://dx.doi.org/10.33889/ijmems.2021.6.1.007.

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This paper proposes a semi-automatic methodology to assist the user in creating surveys about FMEA and Risk Analysis, based on a customized use of the tools for semantic analysis and in particular a home-developed syntactic parser called Kompat Cognitive. The core of this work has been the analysis of the specific FMEA-related jargon and its common modalities of description within scientific papers and patents in order to systematize the linguistic analysis of the reference documents within the proposed step-divided procedure. The main goals of the methodology are to assist not skilled in the art users about FMEA during the analysis of generic and specific features, by considering large moles of contributions in restricted amounts of time. The methodology has then been tested on the same pool of 286 documents, divided between 177 and 109 patents, manually analyzed in our previous survey, in order to replicate part of its classifications through the proposed new modality. In this way we evaluated the abilities of the methodology both to automatically suggesting the main features of interest and to classify the documents according to them.

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22

Yucel, Sezen, Robert J. Moon, Linda J. Johnston, Berkay Yucel, and Surya R. Kalidindi. "Semi-automatic image analysis of particle morphology of cellulose nanocrystals." Cellulose 28, no. 4 (January 21, 2021): 2183–201. http://dx.doi.org/10.1007/s10570-020-03668-8.

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23

Rodrigues, Pedro Miguel, Jorge Luís, and Freni Kekhasharú Tavaria. "Image Analysis Semi-Automatic System for Colony-Forming-Unit Counting." Bioengineering 9, no. 7 (June 22, 2022): 271. http://dx.doi.org/10.3390/bioengineering9070271.

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Background: Accurate quantitative analysis of microorganisms is recognized as an essential tool for gauging safety and quality in microbiology settings in a wide range of fields. The enumeration process of viable microorganisms via traditional culturing techniques are methodically convenient and cost-effective, conferring high applicability worldwide. However, manual counting can be time-consuming, laborious and imprecise. Furthermore, particular cases require an urgent and accurate response for effective processing. Methods: To reduce time limitations and discrepancies, this work introduces an image processing method capable of semi-automatically quantifying the number of colony forming units (CFUs). This rapid enumeration technique enables the technician to provide an expeditious assessment of the microbial load of a given sample. To test and validate the system, three bacterial species were cultured, and a labeled database was created, with subsequent image acquisition. Results: The system demonstrated acceptable classification measures; the mean values of Accuracy, Recall and F-measure were: (1) 95%, 95% and 0.95 for E. coli; (2) 91%, 91% and 0.90 for P. aeruginosa; and (3) 84%, 86% and 0.85 for S. aureus. Conclusions: Evidence related to the time-saving potential of the system was achieved; the time spent on quantification tasks of plates with a high number of colonies might be reduced to a half and occasionally to a third.

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24

Plonis, D., V. Malisauskas, and A. Serackis. "Semi-Automatic Analysis of Gyrotropic Semiconductor Waveguides Using Neural Network." Acta Physica Polonica A 119, no. 4 (April 2011): 542–47. http://dx.doi.org/10.12693/aphyspola.119.542.

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25

Gustafson, Anna, Håkan Schunnesson, Diego Galar, and Uday Kumar. "Production and maintenance performance analysis: manual versus semi‐automatic LHDs." Journal of Quality in Maintenance Engineering 19, no. 1 (March 22, 2013): 74–88. http://dx.doi.org/10.1108/13552511311304492.

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26

di Sciascio, Cecilia, Cecilia Zanni-Merk, Cédric Wemmert, Stella Marc-Zwecker, and François de Bertrand de Beuvron. "Towards a Semi-automatic Semantic Approach for Satellite Image Analysis." Procedia Computer Science 22 (2013): 1388–97. http://dx.doi.org/10.1016/j.procs.2013.11.057.

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27

Márquez, M., M. Silva, and D. Perez-Bendito. "Semi-automatic analysis of mercury in pharmaceuticals by catalytic titration." Journal of Pharmaceutical and Biomedical Analysis 6, no. 3 (January 1988): 307–12. http://dx.doi.org/10.1016/0731-7085(88)80057-8.

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28

Di, Haibin, and Ghassan AlRegib. "Semi‐automatic fault/fracture interpretation based on seismic geometry analysis." Geophysical Prospecting 67, no. 5 (March 13, 2019): 1379–91. http://dx.doi.org/10.1111/1365-2478.12769.

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29

Karacalioglu, Alper, Semra Ince, Mustafa Emer, and Engin Alagoz. "Comparison of Left Ventricular Functional Parameters Calculated by Automatic and Semi-automatic Analysis in MUGA." Gulhane Medical Journal 56, no. 3 (2014): 159. http://dx.doi.org/10.5455/gulhane.45146.

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30

Sandstedt, Mårten, Lilian Henriksson, Magnus Janzon, Gusten Nyberg, Jan Engvall, Jakob De Geer, Joakim Alfredsson, and Anders Persson. "Evaluation of an AI-based, automatic coronary artery calcium scoring software." European Radiology 30, no. 3 (November 14, 2019): 1671–78. http://dx.doi.org/10.1007/s00330-019-06489-x.

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Abstract Objectives To evaluate an artificial intelligence (AI)–based, automatic coronary artery calcium (CAC) scoring software, using a semi-automatic software as a reference. Methods This observational study included 315 consecutive, non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. A semi-automatic and an automatic software obtained the Agatston score (AS), the volume score (VS), the mass score (MS), and the number of calcified coronary lesions. Semi-automatic and automatic analysis time were registered, including a manual double-check of the automatic results. Statistical analyses were Spearman’s rank correlation coefficient (⍴), intra-class correlation (ICC), Bland Altman plots, weighted kappa analysis (κ), and Wilcoxon signed-rank test. Results The correlation and agreement for the AS, VS, and MS were ⍴ = 0.935, 0.932, 0.934 (p < 0.001), and ICC = 0.996, 0.996, 0.991, respectively (p < 0.001). The correlation and agreement for the number of calcified lesions were ⍴ = 0.903 and ICC = 0.977 (p < 0.001), respectively. The Bland Altman mean difference and 1.96 SD upper and lower limits of agreements for the AS, VS, and MS were − 8.2 (− 115.1 to 98.2), − 7.4 (− 93.9 to 79.1), and − 3.8 (− 33.6 to 25.9), respectively. Agreement in risk category assignment was 89.5% and κ = 0.919 (p < 0.001). The median time for the semi-automatic and automatic method was 59 s (IQR 35–100) and 36 s (IQR 29–49), respectively (p < 0.001). Conclusions There was an excellent correlation and agreement between the automatic software and the semi-automatic software for three CAC scores and the number of calcified lesions. Risk category classification was accurate but showing an overestimation bias tendency. Also, the automatic method was less time-demanding. Key Points • Coronary artery calcium (CAC) scoring is an excellent candidate for artificial intelligence (AI) development in a clinical setting. • An AI-based, automatic software obtained CAC scores with excellent correlation and agreement compared with a conventional method but was less time-consuming.

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31

Putri, Renny Eka, Jimmy Aprilio, Santosa, JP Geraldo, and S. Ade. "Semi-Automatic Boom Sprayer Development for Corn Crop Protection." IOP Conference Series: Earth and Environmental Science 1097, no. 1 (October 1, 2022): 012002. http://dx.doi.org/10.1088/1755-1315/1097/1/012002.

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Abstract A semi-automatic boom sprayer was developed to make it easier for farmers to spray pesticides on corn plants. The study was conducted with two treatments at the corn age of 15 days and 30 days. A test was carried out by comparing the performance of the semi-automatic boom sprayer and the performance of the generally used knapsack sprayer. This research also involved evaluation in the laboratory, evaluation in the field, and economic analysis. From the results of technical tests on corn plants aged 15 days and 30 days, it can be concluded that the capacity of the semi-automatic boom sprayer was 0.318 ha/hour with an operator speed of 0.316 m/s. The tank capacity of the semi-automatic boom sprayer was larger than that of the knapsack sprayer. The spraying efficiency of the semi-automatic boom sprayer was greater than that of the knapsack sprayer, namely 86.033% vs. 85.269%, respectively.

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32

Lo Giudice, Antonino, Vincenzo Quinzi, Vincenzo Ronsivalle, Marco Farronato, Carmelo Nicotra, Francesco Indelicato, and Gaetano Isola. "Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandibular Condyle. A Comparative Study Using a Surface-to-Surface Matching Technique." International Journal of Environmental Research and Public Health 17, no. 13 (July 3, 2020): 4789. http://dx.doi.org/10.3390/ijerph17134789.

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The aim of this study was to assess the accuracy of 3D rendering of the mandibular condylar region obtained from different semi-automatic segmentation methodology. A total of 10 Cone beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the condyles by using three free-source software (Invesalius, version 3.0.0, Centro de Tecnologia da Informação Renato Archer, Campinas, SP, Brazil; ITK-Snap, version2.2.0; Slicer 3D, version 4.10.2) and one commercially available software Dolphin 3D (Dolphin Imaging, version 11.0, Chatsworth, CA, USA). The same models were also manually segmented (Mimics, version 17.01, Materialise, Leuven, Belgium) and set as ground truth. The accuracy of semi-automatic segmentation was evaluated by (1) comparing the volume of each semi-automatic 3D rendered condylar model with that obtained with manual segmentation, (2) deviation analysis of each 3D rendered mandibular models with those obtained from manual segmentation. No significant differences were found in the volumetric dimensions of the condylar models among the tested software (p > 0.05). However, the color-coded map showed underestimation of the condylar models obtained with ITK-Snap and Slicer 3D, and overestimation with Dolphin 3D and Invesalius. Excellent reliability was found for both intra-observer and inter-observer readings. Despite the excellent reliability, the present findings suggest that data of condylar morphology obtained with semi-automatic segmentation should be taken with caution when an accurate definition of condylar boundaries is required.

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33

Heinonen, T., P. Dastidar, H. Eskola, H. Frey, P. Ryymin, and E. Laasonen. "Applicability of semi-automatic segmentation for volumetric analysis of brain lesions." Journal of Medical Engineering & Technology 22, no. 4 (January 1998): 173–78. http://dx.doi.org/10.3109/03091909809032536.

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34

Hasegawa, S., R. Shimo, and K. Mihashi. "Semi-Automatic Analysis of Sliding Velocity and Transformation of F-actin." Seibutsu Butsuri 40, supplement (2000): S57. http://dx.doi.org/10.2142/biophys.40.s57_3.

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35

Saifer, Abraham. "FURTHER EXTENSIONS OF A SEMI-AUTOMATIC SYSTEM OF MICROCHEMICAL CLINICAL ANALYSIS*." Annals of the New York Academy of Sciences 102, no. 1 (December 15, 2006): 96–107. http://dx.doi.org/10.1111/j.1749-6632.1962.tb13629.x.

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36

Schrader, Stefan. "Semi-automatic image analysis of earthworm activity in 2D soil sections." Geoderma 56, no. 1-4 (March 1993): 257–64. http://dx.doi.org/10.1016/0016-7061(93)90115-2.

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37

Mulchrone, Kieran F., Patrick A. Meere, and Kingshuk Roy Choudhury. "SAPE: a program for semi-automatic parameter extraction for strain analysis." Journal of Structural Geology 27, no. 11 (November 2005): 2084–98. http://dx.doi.org/10.1016/j.jsg.2005.05.019.

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38

Aligholi, Saeed, Gholam Reza Lashkaripour, and Mohammad Ghafoori. "Estimating engineering properties of igneous rocks using semi-automatic petrographic analysis." Bulletin of Engineering Geology and the Environment 78, no. 4 (May 16, 2018): 2299–314. http://dx.doi.org/10.1007/s10064-018-1305-7.

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39

Mikecz, P., Gy Toth, M. Dodd, F. Chaloner, N. Evans, and P. F. Sharp. "21. Quantitative, semi-automatic analysis of thin-layer chromotographic (TLC) plates." Nuclear Medicine Communications 17, no. 4 (April 1996): 295. http://dx.doi.org/10.1097/00006231-199604000-00136.

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40

Thapa, Bhaskar B., Paul Hughett, and Kenzi Karasaki. "Semi‐automatic analysis of rock fracture orientations from borehole wall images." GEOPHYSICS 62, no. 1 (January 1997): 129–37. http://dx.doi.org/10.1190/1.1444112.

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We develop a semiautomatic method of identifying rock fractures and analyzing their orientations from digital images of borehole walls. This method is based on an algorithm related to the Hough transform which is modified to find sinusoidal rather than linear patterns. The algorithm uses the high‐intensity contrast between the fracture aperture and the rock wall, as well as the sinusoidal trajectory defined by the intersection of the borehole and the fracture. The analysis rate of the algorithm itself is independent of fracture contrast and network complexity. The method has successfully identified fractures both in test cases containing several fractures in a noisy background and in real borehole images. The analysis rate was 0.3–1.2 minutes/m of input data, compared to an average of 12 minutes/m using an existing interactive method. An automatic version under development should open new possibilities for site characterization, such as real‐time exploration and analysis of tunnel stability and support requirements as construction proceeds.

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Jansen, Jesper, Ruud Schreurs, Leander Dubois, Thomas J. J. Maal, Peter J. J. Gooris, and Alfred G. Becking. "Orbital volume analysis: validation of a semi-automatic software segmentation method." International Journal of Computer Assisted Radiology and Surgery 11, no. 1 (July 16, 2015): 11–18. http://dx.doi.org/10.1007/s11548-015-1254-6.

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Dallant, P., A. Meunier, P. S. Christel, and L. Sedel. "Semi-automatic image-analysis applied to the quantification of bone microstructure." Journal of Biomedical Engineering 8, no. 4 (October 1986): 320–28. http://dx.doi.org/10.1016/0141-5425(86)90064-6.

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Heinone, T., P. Dastidar, P. Kauppinen, J. Malmivuo, and H. Eskola. "Semi-automatic tool for segmentation and volumetric analysis of medical images." Medical & Biological Engineering & Computing 36, no. 3 (May 1998): 291–96. http://dx.doi.org/10.1007/bf02522473.

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Renisch, S., R. Opfer, T. Derlin, R. Buchert, I. C. Carlsen, W. Brenner, and I. Apostolova. "FDG PET/CT in cancer therapy monitoring." Nuklearmedizin 50, no. 02 (2011): 83–92. http://dx.doi.org/10.3413/nukmed-0314-10-05.

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SummaryObjectives: We developed and tested a software tool for computer-assisted analysis of FDG-PET/CT in cancer therapy monitoring. The tool provides automatic semi-quantitative analysis of a baseline scan together with up to two follow-up scans (standardized uptake values, glycolytic volume). The tool also supports visual analysis by local spatial registration which allows display of tumor lesions with the same orientation in all scans. The tool’s stability and accuracy was tested at typical everyday image quality. Patients, methods: Ten unselected cancer patients in whom three FDG PET/CT scans had been performed were included. A total of 18 lesions were analyzed. Results: Automatic lesion tracking worked properly in all lesions but one. In this lesion local coregistration had to be adjusted manually tuwhich, however, is easily performed with the tool. Semi-automatic lesion segmentation and fully automatic semi-quantitative analysis worked properly in all cases. Computer-assisted analysis was significantly less time consuming than manual analysis. Conclusions: The novel software tool appears useful for analysis of FDGPET/ CT in cancer therapy monitoring in clinical routine patient care.

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Lo Giudice, Antonino, Vincenzo Ronsivalle, Cristina Grippaudo, Alessandra Lucchese, Simone Muraglie, Manuel O. Lagravère, and Gaetano Isola. "One Step before 3D Printing—Evaluation of Imaging Software Accuracy for 3-Dimensional Analysis of the Mandible: A Comparative Study Using a Surface-to-Surface Matching Technique." Materials 13, no. 12 (June 21, 2020): 2798. http://dx.doi.org/10.3390/ma13122798.

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The accuracy of 3D reconstructions of the craniomaxillofacial region using cone beam computed tomography (CBCT) is important for the morphological evaluation of specific anatomical structures. Moreover, an accurate segmentation process is fundamental for the physical reconstruction of the anatomy (3D printing) when a preliminary simulation of the therapy is required. In this regard, the objective of this study is to evaluate the accuracy of four different types of software for the semiautomatic segmentation of the mandibular jaw compared to manual segmentation, used as a gold standard. Twenty cone beam computed tomography (CBCT) with a manual approach (Mimics) and a semi-automatic approach (Invesalius, ITK-Snap, Dolphin 3D, Slicer 3D) were selected for the segmentation of the mandible in the present study. The accuracy of semi-automatic segmentation was evaluated: (1) by comparing the mandibular volumes obtained with semi-automatic 3D rendering and manual segmentation and (2) by deviation analysis between the two mandibular models. An analysis of variance (ANOVA) was used to evaluate differences in mandibular volumetric recordings and for a deviation analysis among the different software types used. Linear regression was also performed between manual and semi-automatic methods. No significant differences were found in the total volumes among the obtained 3D mandibular models (Mimics = 40.85 cm3, ITK-Snap = 40.81 cm3, Invesalius = 40.04 cm3, Dolphin 3D = 42.03 cm3, Slicer 3D = 40.58 cm3). High correlations were found between the semi-automatic segmentation and manual segmentation approach, with R coefficients ranging from 0,960 to 0,992. According to the deviation analysis, the mandibular models obtained with ITK-Snap showed the highest matching percentage (Tolerance A = 88.44%, Tolerance B = 97.30%), while those obtained with Dolphin 3D showed the lowest matching percentage (Tolerance A = 60.01%, Tolerance B = 87.76%) (p < 0.05). Colour-coded maps showed that the area of greatest mismatch between semi-automatic and manual segmentation was the condylar region and the region proximate to the dental roots. Despite the fact that the semi-automatic segmentation of the mandible showed, in general, high reliability and high correlation with the manual segmentation, caution should be taken when evaluating the morphological and dimensional characteristics of the condyles either on CBCT-derived digital models or physical models (3D printing).

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Ding, Zhong Qiu. "The Semi-Automatic Restoration of Regular Paper Fragments." Applied Mechanics and Materials 596 (July 2014): 79–82. http://dx.doi.org/10.4028/www.scientific.net/amm.596.79.

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The restoration of regular paper fragments can be solved by edge matching. Edge matching is based primarily on the length and location of the break in writing, however, it is possible that the edges of some regular paper fragments are blank. And so the error of restoration is the inevitable. In this case, it needs manual intervention based on the article content and then eliminate errors. Prior to the establishment of a mathematical model, there is a need for binarization of regular paper fragments with Matlab, then establish vector space model and construct edge contrast matrix, get the order after the Q cluster analysis of fragments. Finally the original picturecan be obtained through splicing and restoring in the order.

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Stroe, Sergiu, Constantin Onescu, Ștefan Tabacu, and Inal Cagri. "Semi-Automatic Assembly Device for Car Stepped Door Stop." Applied Mechanics and Materials 808 (November 2015): 345–50. http://dx.doi.org/10.4028/www.scientific.net/amm.808.345.

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The paper shows a new approach of the assembly device design, as a combination of product complexity analysis, the level of automation imposed by the company business strategy and assembly device complexity analysis. The proposed assembly process flowchart contains a hybrid method to assembly balls, springs, plastic covers and safety locks into the box of the stepped mechanisms of car door. To increase the productivity correlated with costs reduction, it chooses a semi-automatic method based on the PLC automatics which commands with the help of pneumatics. The device assembly principles will be established and with the help CAD software the device model will be performed.

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Laux, Dennis, and Andreas Henk. "Terrestrial laser scanning and fracture network characterisation – perspectives for a (semi-) automatic analysis of point cloud data from outcrops." Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 166, no. 1 (March 1, 2015): 99–118. http://dx.doi.org/10.1127/1860-1804/2015/0089.

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Csorba, Kristóf, Ádám Budai, Judit Zöldföldi, and Balázs Székely. "GrainAutLine: an Environment for Semi-Automatic Processing of Marble Thin Section Images." Periodica Polytechnica Electrical Engineering and Computer Science 61, no. 4 (August 17, 2017): 305. http://dx.doi.org/10.3311/ppee.10890.

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GrainAutLine is an interdisciplinary microscopy image analysis tool with domain specific smart functions to partially automate the processing of marble thin section images. It allows the user to create a clean grain boundary image which is a starting point of several archaeometric and geologic analyses. The semi-automatic tools minimize the need for carefully drawing the grain boundaries manually, even in cases where twin crystals prohibit the use of classic edge detection based boundary detection. Due to the semi-automatic approach, the user has full control over the process and can modify the automatic results before finalizing a specific step. This approach guarantees high quality results both in cases where the process is easy to automate, and also if it needs more help from the user. This paper presents the basic operation of the system and details about the provided tools as a case study for an interdisciplinary, semi-automatic image processing application.

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Song, Young Ju, Hyo Sung Kwak, Gyung Ho Chung, and Seongil Jo. "Quantification of Carotid Intraplaque Hemorrhage: Comparison between Manual Segmentation and Semi-Automatic Segmentation on Magnetization-Prepared Rapid Acquisition with Gradient-Echo Sequences." Diagnostics 9, no. 4 (November 11, 2019): 184. http://dx.doi.org/10.3390/diagnostics9040184.

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Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

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