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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Feb 2018
Next issue: May 2018
Avg review time: 105 days


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

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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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.

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  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: 622 | Views: 3,377

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

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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.

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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

[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

[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

[4] Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system, Al-masni, Mohammed A., Al-antari, Mugahed A., Park, Jeong-Min, Gi, Geon, Kim, Tae-Yeon, Rivera, Patricio, Valarezo, Edwin, Choi, Mun-Taek, Han, Seung-Moo, Kim, Tae-Seong, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue , 2018.
Digital Object Identifier: 10.1016/j.cmpb.2018.01.017

[5] 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

[6] 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

[7] 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

[8] 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

[9] 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

[10] 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

[11] 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

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

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