|1/2013 - 15|
Segmentation of Bone Structure in X-ray Images using Convolutional Neural NetworkCERNAZANU-GLAVAN, C. , HOLBAN, S.
|Click to see author's profile on SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,269 KB) | Citation | Downloads: 597 | Views: 3,108|
image segmentation, neural network, convolution, biomedical image processing
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
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.
Web of Science® Times Cited: 8 [View]
View record in Web of Science® [View]
View Related Records® [View]
SCOPUS® Times Cited: 14
View record in SCOPUS® [Free preview]
 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]
 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]
 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]
 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]
 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]
 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]
 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, 2017.
Digital Object Identifier: 10.1016/j.medengphy.2017.08.016 [CrossRef]
 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]
 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]
 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]
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.
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.