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A Neural Network Approach to Medical Image Segmentation and Three-Dimensional Reconstruction

  • Vitoantonio Bevilacqua
  • Giuseppe Mastronardi
  • Mario Marinelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

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.

Keywords

Segmentation Technique Neural Network Approach Segmented Region Streak Artifact Adaptive Resonance Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vitoantonio Bevilacqua
    • 1
  • Giuseppe Mastronardi
    • 1
  • Mario Marinelli
    • 1
  1. 1.Dipartimento di Elettrotecnica ed ElettronicaPolytechnic of BariBariItaly

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