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Segmentation of Bone Structure in X-ray Images using Convolutional Neural NetworkCERNAZANU-GLAVAN, C. , HOLBAN, S.
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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)
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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 813] [SCOPUS Times Cited 1003]
 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 2897] [SCOPUS Times Cited 4198]
 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.
[CrossRef] [SCOPUS Times Cited 493]
 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 21]
 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 145]
 M. Riesenhuber, T. Poggio, "Hierarchical models of object recognition in cortex", Nature Neuroscience, vol. 2, pp. 1019-1025, 1999.
[CrossRef] [SCOPUS Times Cited 1611]
 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
[CrossRef] [SCOPUS Times Cited 494]
 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 18] [SCOPUS Times Cited 37]
 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 8] [SCOPUS Times Cited 19]
 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.
[CrossRef] [SCOPUS Times Cited 3]
 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] [SCOPUS Times Cited 3]
 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] [SCOPUS Times Cited 6]
 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 107] [SCOPUS Times Cited 159]
 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] [SCOPUS Times Cited 9]
 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.
[CrossRef] [SCOPUS Times Cited 1]
 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.
[CrossRef] [SCOPUS Times Cited 2182]
 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 37] [SCOPUS Times Cited 58]
 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
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