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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2013 - 15

 HIGH-IMPACT PAPER 

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
 
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Download PDF pdficon (1,269 KB) | Citation | Downloads: 1,122 | Views: 5,915

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

Abstract
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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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