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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 76 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  1/2008 - 10

Ontology-Based Knowledge Organization for the Radiograph Images Segmentation

MATEI, O. See more information about MATEI, O. on SCOPUS See more information about MATEI, O. on IEEExplore See more information about MATEI, O. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,103 KB) | Citation | Downloads: 789 | Views: 3,052

Author keywords
normalization, onotologies, radiographs

References keywords
medical(12), chest(12), segmentation(8), radiographs(8), lung(8), digital(7), physics(6), analysis(6), imaging(5), feature(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2008-04-02
Volume 8, Issue 1, Year 2008, On page(s): 56 - 61
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2008.01010
Web of Science Accession Number: 000259903500010
SCOPUS ID: 70349189190

Abstract
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The quantity of thoracic radiographies in the medical field is ever growing. An automated system for segmenting the images would help doctors enormously. Some approaches are knowledge-based; therefore we propose here an ontology for this purpose. Thus it is machine oriented, rather than human-oriented. That is all the structures visible on a thoracic image are described from a technical point of view.


References | Cited By  «-- Click to see who has cited this paper

[1] Nci ontology, [Online] Available: Temporary on-line reference link removed - see the PDF document

[2] Galen ontology, [Online] Available: Temporary on-line reference link removed - see the PDF document

[3] S. G. Armato and H. Giger, M. L.and MacMahon, "Automated lung segmentation in digitized postero-anterior chest radiographs", Academic Radiology, (4):245-255, 1998. [PubMed]

[4] M. S. Brown, L.S. Wilson, B.D. Doust, R.W. Gill, and C. Sun., "Knowledgebased method for segmentation and analysis of lung boundaries in chest x-ray images", Computerized Medical Imaging and Graphics", 22:463-477, 1998. [PubMed]

[5] J. J. Cimino, Hricsak G., Johnson S. B., and Clayton P. D., "Designing an introspective multipurpose controlled medical vocabulary", In Proc 13th AnnuSymp Comput Appl Med Care, pp. 513-517, 1989. [PubMed]

[6] J. Duryea and J.M. Boone, "A fully automatic algorithm for the segmentation of lung fields in digital chest radiographic images", Medical Physics, 2(22):183-191, 1995. [PubMed]

[7] B. van Ginneken, A. F. Frangi, J. J. Staal, B. M. ter Haar Romeny, and M. A. Viergever, "Active shape model segmentation with optimal features", IEEE Transactions on Medical Imaging, 21(8):924-933, 2002.
[CrossRef] [Web of Science Times Cited 201] [SCOPUS Times Cited 343]


[8] B. van Ginneken, Haar Romeny B. M. ter Katsuragawa, S., K. Doi, and M. A. Viergever, "Automatic detection of abnormalities in chest radiographs using local texture analysis", IEEE Transactions on Medical Imaging, 21(2):139-149, 2002.
[CrossRef] [Web of Science Times Cited 76]


[9] H. Gu, M. Halper, J. Geller, and Y. Perl, "Benefits of an object-oriented database representation for controlled medical terminologies", J Am Med Inform Assoc, (6):283303, 1999. [PubMed]

[10] L. Li, Y. Zheng, M. Kallergi, and R. A. Clark, "Improved method for automatic identification of lung regions on chest radiographs", Academic Radiology, 7(8):629-638, 2001. [PubMed]

[11] M. Loog and B. van Ginneken, "Supervised segmentation by iterated contextual pixel classification", In In Proceedings 16th International Conference on Pattern Recognition, pages 925-928, 2002. [PubMed]

[12] M. F. McNitt-Gray, H. K. Huang, and J. W. Sayre, "Feature selection in the pattern classification problem of digital chest radiograph segmentation", IEEE Transactions on Medical Imaging, 14(3):537- 547, 1995.
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 89]


[13] N. Nakamori, K. Doi, V. Sabeti, and H. MacMahon, "Image feature analysis and computer-aided diagnosis in digital radiography: automated analysis of sizes of heart and lung in chest images", Medical Physics, 17(3):342-350, 1990. [PubMed]

[14] E. Pietka, "Lung segmentation in digital chest radiographs", Journal of Digital Imaging, 2:79-84, 1994.

[15] C. Rosse, J. L. Mejino, B. R. Modayur, R. Jakobovits, K. P. Hinshaw, and J. F. Brinkley, "Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base", J Am Med Inform Assoc, (5):17-40, 1998. [PubMed]

[16] C. Rosse, L. G. Shapiro, and J. F. Brinkley, "The digital anatomist foundational model: principles for defining and structuring its concept domain", In Proc AMIA Symp, pages 820-824, 1998. [PubMed]

[17] O. Tsujii, M. T. Freedman, and S. K. Mun, "Automated segmentation of anatomic regions in chest radiographs using an adaptive-sized hybrid neural network", Medical Physics, 25(6):998-1007, 1998. [PubMed]

[18] N. F. Vittitoe, R. Vargas-Voracek, and C. E. Floyd Jr., "Identification of lung regions in chest radiographs using markov random field modeling", Medical Physics, 25(6):976-985, 1998. [PubMed]

[19] X. W. Xu and K. Doi, "Image feature analysis for computer-aided diagnosis: accurate determination of ribcage boundary in chest radiographs", Medical Physics, 5(22):617-626, 1995. [PubMed]

[20] X. W. Xu and K. Doi, "Image feature analysis for computer-aided diagnosis: detection of right and left hemidiaphragm edges and delineation of lung field in chest radiographs", Medical Physics, 9(23):1613-1624, 1996. [PubMed]

References Weight

Web of Science® Citations for all references: 364 TCR
SCOPUS® Citations for all references: 432 TCR

Web of Science® Average Citations per reference: 18 ACR
SCOPUS® Average Citations per reference: 22 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-09-19 19:14 in 23 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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