A neural network approach to medical image segmentation and three-dimensional reconstruction

Authors:
Vitoantonio Bevilacqua;Giuseppe Mastronardi;Mario Marinelli
Affiliations:
Dipartimento di Elettrotecnica ed Elettronica, Polytechnic of Bari, Bari, Italy;Dipartimento di Elettrotecnica ed Elettronica, Polytechnic of Bari, Bari, Italy;Dipartimento di Elettrotecnica ed Elettronica, Polytechnic of Bari, Bari, Italy
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

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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.