|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 in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,269 KB) | Citation | Downloads: 734 | Views: 4,293|
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.
|References|||||Cited By «-- Click to see who has cited this paper|
| K. Fukushima, "Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position", Biological Cybernetics, vol. 36, pp. 193-202, 1980. |
[CrossRef] [Web of Science Times Cited 1235]
 Y. LeCun, L. Bottou, Y. Bengio and P. Haffner, "Gradient-Based Learning Applied to Document Recognition", Proceedings of the IEEE, 86(11), pp. 2278-2324, November 1998.
[CrossRef] [Web of Science Times Cited 7953]
 P. Y. Simard, D. Steinkraus, J. C. Platt, "Best practices for convolutional neural networks applied to visual document analysis", Document Analysis and Recognition, 2003, Proceedings. Seventh International Conference on, pp. 958- 963, 3-6 Aug. 2003.
 J. Schmidhuber, M. Eldracher, B. Foltin, "Semilinear predictability minimization produces well-known feature detectors", Neural Computation, vol. 8, pp. 773-786, 1996.
[CrossRef] [Web of Science Times Cited 22]
 P. O. Hoyer, A. Hyvarinen, "Independent Component Analysis Applied to Feature Extraction from Colour and Stereo Images", Network:Computation in Neural Systems, vol. 11, pp. 191-210, 2000.
[CrossRef] [Web of Science Times Cited 164]
 M. Riesenhuber, T. Poggio, "Hierarchical models of object recognition in cortex", Nature Neuroscience, vol. 2, pp. 1019-1025, 1999.
 D. Scherer, A. Muller, S. Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition", in International Conference on Articial Neural Networks, pp. 82-91, Springer, 2010
 D. Ciresan, U. Meier, J. Schmidhuber, "Multi-column Deep Neural Networks for Image Classification", in Computer Vision and Pattern Recognition, CVPR 2012, pp. 3642-3649
 D. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber, "Deep Neural Networks Segment Neuronal Membranes", in Electron Microscopy Images, NIPS 2012
 D. Strigl, K. Kofler, S. Podlipnig, "Performance and scalability of GPU-based convolutional neural networks", in PDP '10 Proceedings of the 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing, pp. 317-324, 2010.
[CrossRef] [Web of Science Times Cited 57]
 U. Meier, C. D. Ciresan, L. M. Gambardella, J. Schmidhuber, "Better digit recognition with a committee of simple neural nets", in Document Analysis and Recognition (ICDAR), 2011 International Conference on, pp.1250-1254, 18-21 Sept. 2011.
[CrossRef] [Web of Science Times Cited 29]
 H. Hu, H. Liu, L. Chen, C. C. Hung, X. Xu, Z. Lan, "Image segmentation of cervical vertebra in X-ray radiographs using the curve fitting strategy", in Proceedings of the 2011 ACM Symposium on Applied Computing (SAC '11). ACM, New York, pp. 853-858, 2011.
 E. Ganea, D. D. Burdescu, M. Brezovan, "New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure", Advances in Electrical and Computer Engineering, vol. 11, no. 4, pp. 111-116, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 2]
 Q. Wei, H. Xiaolei, J. Yuanyuan, "Segmentation in noisy medical images using PCA model based particle filtering", in Medical Imaging 2008: Image Processing, Proceedings of the SPIE, vol. 6914, 2008.
[CrossRef] [Web of Science Record]
 B. N. Li, C. K. Chui, S. Chang, S.H. Ong, "Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation", in Computers in Biology and Medicine, vol. 41, issue 1, pp. 1-10, 2011.
[CrossRef] [Web of Science Times Cited 196]
 N. Boukala, E. Favier, B. Laget, P. Radeva, "Active shape model based segmentation of bone structures in hip radiographs", in Industrial Technology, ICIT '04 IEEE International Conference on, pp. 1682-1687, vol. 3, 2004.
 O. Matei, "Ontology-Based Knowledge Organization for the Radiograph Images Segmentation", Advances in Electrical and Computer Engineering, vol. 8 (15), no. 1 (29), 2008.
[CrossRef] [Full Text] [Web of Science Times Cited 7]
 A. Alvarenga de Moura Meneses, A. P. de Almeida, J. Soares, P. Azambuja, M. S. Gonzalez, S. Cardoso, D. Braz, C. E. de Almeida, R. C. Barroso, "Segmentation of X-ray micro-computed tomography using Neural Networks trained with Statistical Information: Application to biomedical images", in Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pp.3999-4001, 2011.
 D. H. Hubel, T. N. Wiesel, "Receptive fields of single neurons in the cat's striate cortex", Journal of Physiology, vol. 148 , pp. 574-591, 1959
 D. H. Hubel, T. N. Wiesel, "Receptive fields, binocular interaction,and functional architecture in the cat's visual cortex", Journal of Physiology, vol. 160(1) , pp. 106-154, 1962
 A. Krizhevsky, "Convolutional deep belief networks on CIFAR-10", Technical report, University of Toronto, Aug. 2010.
 W. M. Rand, "Objective criteria for the evaluation of clustering methods", Journal of the American Statistical association, vol. 66(336), pp. 846-850, 1971.
 V. Jain, B. Bollmann, M. Richardson, D. R. Berger, M. Helmstaedter, K. L. Briggman, W. Denk, J. B. Bowden, J. M. Mendenhall, W. C. Abraham, K. M. Harris, N. Kasthuri, K. J. Hayworth, R. Schalek, J. C. Tapia, J. W. Lichtman, H. S. Seung, "Boundary Learning by Optimization with Topological Constraints", in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on , pp.2488-2495, 13-18 June 2010.
[CrossRef] [Web of Science Times Cited 49]
 S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber, "Gradient flow in recurrent nets: the difficulty of learning long-term dependencies", in A Field Guide to Dynamical Recurrent Neural Networks, IEEE press, 2001.
 C. Cernazanu-Glavan, S. Holban, "Bone contour segmentation in radiograph images", Scientific Bulletin of "Politehnica" University of Timisoara, Romania, Transactions on Automatic Control and Computer Science, to be published
Web of Science® Citations for all references: 9,714 TCR
SCOPUS® Citations for all references: 0
Web of Science® Average Citations per reference: 374 ACR
SCOPUS® Average Citations per reference: 0
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 2019-08-20 02:14 in 133 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
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.