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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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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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.

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.

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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 in 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: 687 | Views: 3,943

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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[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.
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[3] Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning, Shashank Kaira, C., Yang, Xiaogang, De Andrade, Vincent, De Carlo, Francesco, Scullin, William, Gursoy, Doga, Chawla, Nikhilesh, Materials Characterization, ISSN 1044-5803, Issue , 2018.
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[5] Statistical and Physical Micro-Feature-Based Segmentation of Cortical Bone Images Using Artificial Intelligence, Hage, Ilige S., Hamade, Ramsey F., Materials Science Forum, ISSN 1662-9752, Issue , 2014.
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[6] Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks, Al-masni, Mohammed A., Al-antari, Mugahed A., Choi, Mun-Taek, Han, Seung-Moo, Kim, Tae-Seong, Computer Methods and Programs in Biomedicine, ISSN 0169-2607, Issue , 2018.
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[9] 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.
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[10] 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.
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[11] 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.
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[12] 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.
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[13] 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

[14] Vertebrae Segmentation from X-ray Images Using Convolutional Neural Network, Kuok, Chan-Pang, Fu, Min-Jun, Lin, Chii-Jen, Horng, Ming-Huwi, Sun, Yung-Nien, Proceedings of the 2018 International Conference on Information Hiding and Image Processing - IHIP 2018, ISBN 9781450365468, 2018.
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[15] 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.
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[16] 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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