Abstract
Medical Image Analysis represents a very important step in clinical diagnosis. It provides image segmentation of the Region of Interest (ROI) and the generation of a three-dimensional model, representing the selected object. In this work, was proposed a neural network segmentation based on Self-Organizing Maps (SOM) and a three-dimensional SOM architecture to create a 3D model, starting from 2D data of extracted contours. The utilized dataset consists of a set of CT images of patients presenting a prosthesis’ implant, in DICOM format. An application was developed in Visual C++, which provides an user interface to visualize DICOM images and relative segmentation. Moreover it generates a three-dimensional model of the segmented region using Direct3D.
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Bevilacqua, V., Mastronardi, G., Marinelli, M. (2006). A Neural Network Approach to Medical Image Segmentation and Three-Dimensional Reconstruction. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_3
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DOI: https://doi.org/10.1007/11816157_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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