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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

Segmentation of Bone Structure in X-ray Images using Convolutional Neural Network