Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Nov 2017
Next issue: Feb 2018
Avg review time: 104 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


TRAFFIC STATS

1,792,027 unique visits
514,158 downloads
Since November 1, 2009



No robots online now


SJR SCImago RANK

SCImago Journal & Country Rank


SEARCH ENGINES

aece.ro - Google Pagerank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
 Volume 16 (2016)
 
     »   Issue 4 / 2016
 
     »   Issue 3 / 2016
 
     »   Issue 2 / 2016
 
     »   Issue 1 / 2016
 
 
 Volume 15 (2015)
 
     »   Issue 4 / 2015
 
     »   Issue 3 / 2015
 
     »   Issue 2 / 2015
 
     »   Issue 1 / 2015
 
 
 Volume 14 (2014)
 
     »   Issue 4 / 2014
 
     »   Issue 3 / 2014
 
     »   Issue 2 / 2014
 
     »   Issue 1 / 2014
 
 
  View all issues  


FEATURED ARTICLE

Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
Issue 3/2017

AbstractPlus






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.

Read More »


    
 

  1/2013 - 15

Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network

CERNAZANU-GLAVAN, C. See more information about CERNAZANU-GLAVAN, C. on SCOPUS See more information about CERNAZANU-GLAVAN, C. on IEEExplore See more information about CERNAZANU-GLAVAN, C. on Web of Science, HOLBAN, S. See more information about HOLBAN, S. on SCOPUS See more information about HOLBAN, S. on SCOPUS See more information about HOLBAN, S. 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 (1,269 KB) | Citation | Downloads: 611 | Views: 3,206

Author keywords
image segmentation, neural network, convolution, biomedical image processing

References keywords
neural(11), recognition(9), segmentation(8), networks(8), images(6), schmidhuber(5), image(5), medical(4), cvpr(4), convolutional(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-02-28
Volume 13, Issue 1, Year 2013, On page(s): 87 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.01015
Web of Science Accession Number: 000315768300015
SCOPUS ID: 84875328950

Abstract
Quick view
Full text preview
The segmentation process represents a first step necessary for any automatic method of extracting information from an image. In the case of X-ray images, through segmentation we can differentiate the bone tissue from the rest of the image. There are nowadays several segmentation techniques, but in general, they all require the human intervention in the segmentation process. Consequently, this article proposes a new segmentation method for the X-ray images using a Convolutional Neural Network (CNN). In present, the convolutional networks are the best techniques for image segmentation. This fact is demonstrated by their wide usage in all the fields, including the medical one. As the X-ray images have large dimensions, for reducing the training time, the method proposed by the present article selects only certain areas (maximum interest areas) from the entire image. The neural network is used as pixel classifier thus causing the label of each pixel (bone or none-bone) from a raw pixel values in a square area. We will also present the method through which the network final configuration was chosen and we will make a comparative analysis with other 3 CNN configurations. The network chosen by us obtained the best results for all the evaluation metrics used, i.e. warping error, rand error and pixel error.


References | Cited By

Cited-By ISI Web of Science

Web of Science® Times Cited: 8 [View]
View record in Web of Science® [View]
View Related Records® [View]

Updated 3 days, 4 hours ago


Cited-By CrossRef

SCOPUS® Times Cited: 16
View record in SCOPUS®
[Free preview]

Updated 3 days, 4 hours ago

Cited-By CrossRef

[1] An approach for chest tube detection in chest radiographs, Mercan, Cem Ahmet, Celebi, Mustafa Serdar, IET Image Processing, ISSN 1751-9659, Issue 2, Volume 8, 2014.
Digital Object Identifier: 10.1049/iet-ipr.2013.0239
[CrossRef]

[2] Detection of concealed cars in complex cargo X-ray imagery using Deep Learning, Jaccard, Nicolas, Rogers, Thomas W., Morton, Edward J., Griffin, Lewis D., Journal of X-Ray Science and Technology, ISSN 0895-3996, Issue 3, Volume 25, 2017.
Digital Object Identifier: 10.3233/XST-16199
[CrossRef]

[3] Deep Learning in Medical Imaging: General Overview, Lee, June-Goo, Jun, Sanghoon, Cho, Young-Won, Lee, Hyunna, Kim, Guk Bae, Seo, Joon Beom, Kim, Namkug, Korean Journal of Radiology, ISSN 1229-6929, Issue 4, Volume 18, 2017.
Digital Object Identifier: 10.3348/kjr.2017.18.4.570
[CrossRef]

[4] Automatic 3D liver location and segmentation via convolutional neural network and graph cut, Lu, Fang, Wu, Fa, Hu, Peijun, Peng, Zhiyi, Kong, Dexing, International Journal of Computer Assisted Radiology and Surgery, ISSN 1861-6410, Issue 2, Volume 12, 2017.
Digital Object Identifier: 10.1007/s11548-016-1467-3
[CrossRef]

[5] Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc, Al-Bander, Baidaa, Al-Nuaimy, Waleed, Williams, Bryan M., Zheng, Yalin, Biomedical Signal Processing and Control, ISSN 1746-8094, Issue , 2018.
Digital Object Identifier: 10.1016/j.bspc.2017.09.008
[CrossRef]

[6] A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images, Wang, Yunzhi, Qiu, Yuchen, Thai, Theresa, Moore, Kathleen, Liu, Hong, Zheng, Bin, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue , 2017.
Digital Object Identifier: 10.1016/j.cmpb.2017.03.017
[CrossRef]

[7] Iterative approach for 3D reconstruction of the femur from un-calibrated 2D radiographic images, Youn, Kibeom, Park, Moon Seok, Lee, Jehee, Medical Engineering & Physics, ISSN 1350-4533, Issue , 2017.
Digital Object Identifier: 10.1016/j.medengphy.2017.08.016
[CrossRef]

[8] Discriminative feature extraction from X-ray images using deep convolutional neural networks, Srinivas, M., Roy, Debaditya, Mohan, C. Krishna, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), ISBN 978-1-4799-9988-0, 2016.
Digital Object Identifier: 10.1109/ICASSP.2016.7471809
[CrossRef]

[9] CCS Coding of Discharge Diagnoses via Deep Neural Networks, Helwe, Chadi, Elbassuoni, Shady, Geha, Mirabelle, Hitti, Eveline, Makhlouf Obermeyer, Carla, Proceedings of the 2017 International Conference on Digital Health - DH '17, ISBN 9781450352499, 2017.
Digital Object Identifier: 10.1145/3079452.3079498
[CrossRef]

[10] Deep neural networks for anatomical brain segmentation, de Brebisson, Alexandre, Montana, Giovanni, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), ISBN 978-1-4673-6759-2, 2015.
Digital Object Identifier: 10.1109/CVPRW.2015.7301312
[CrossRef]

Updated 3 days, 4 hours ago

Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.

Web of Science® is a registered trademark of Thomson Reuters, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.


Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: