PURPOSE: Dual-energy x-ray absorptiometry (DXA) is the "golden standard" for diagnosing osteoporosis. Its analyzing algorithm (software) makes it possible to distinguish the bone from the soft tissue. Until now there are only attempts to process and acquire images using automatic segmentation with convolutional neural networks (CNN). Machine reconstruction and precise specific models of anatomic structures from medical images could be accomplished using computer vision. The objective of the

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... ent work is to introduce the potential of the two computer methods and their application in the diagnostic DXA analysis. METHODS: DXA generates a report in the DICOM format which includes patient data (age, gender, height, weight, bone mineral density, T-score and Z-score) and an image of the scanned spine as well as the region of interest (ROI). The CNN methods are based mainly on intermediate analysis. The learning of the segmentation of CNN by generating segmentation labels using simple heuristic is done using computer vision. The functions of the loss and the architecture of the CNN is then determined. In that manner the right analysis of the existing medical image is made possible. RESULTS: The computer library OpenCV is the way to realize a model for the assessment of a DXA analysis. The library is available for Python programming language. The library has functions for the extraction of colour objects, image smoothing, Canny's edge detector, Hough transform and methods for work with contours. CONCLUSIONS: The detection and extraction of images is fundamental for the analysis of DXA which is a step forward in the precision of the in-vivo diagnostic of the bone.