Segmentation of bone structure in X-ray images using convolutional neural network

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 di ...

[1]  H. Sebastian Seung,et al.  Boundary Learning by Optimization with Topological Constraints , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Klaus Kofler,et al.  Performance and Scalability of GPU-Based Convolutional Neural Networks , 2010, 2010 18th Euromicro Conference on Parallel, Distributed and Network-based Processing.

[3]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[4]  B. Laget,et al.  Active shape model based segmentation of bone structures in hip radiographs , 2004, 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT '04..

[5]  Anderson Alvarenga de Moura Meneses,et al.  Segmentation of X-ray micro-computed tomography using Neural Networks trained with Statistical Information: Application to biomedical images , 2011, 2011 IEEE Nuclear Science Symposium Conference Record.

[6]  Yuanyuan Jia,et al.  Segmentation in noisy medical images using PCA model based particle filtering , 2008, SPIE Medical Imaging.

[7]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[8]  Jürgen Schmidhuber,et al.  Semilinear Predictability Minimization Produces Well-Known Feature Detectors , 1996, Neural Computation.

[9]  Oliviu Matei.  Ontology-Based Knowledge Organization for the Radiograph Images Segmentation , 2008 .

[10]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[11]  Hong Liu,et al.  Image segmentation of cervical vertebra in X-ray radiographs using the curve fitting strategy , 2011, SAC '11.

[12]  Luca Maria Gambardella,et al.  Better Digit Recognition with a Committee of Simple Neural Nets , 2011, 2011 International Conference on Document Analysis and Recognition.

[13]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[14]  Eugen Ganea,et al.  New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure , 2011 .

[15]  Sim Heng Ong,et al.  Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation , 2011, Comput. Biol. Medicine.

[16]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[18]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[19]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[20]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[21]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[22]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.