1995 / Solid Freeform Fabrication Proceedings
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56. A Neural Network Architecture to Identify the Bone Tissue for Solid Freeform Fabrication

Author Nena Marin, Richard Crawford, The University of Texas at Austin

Source Solid Freeform Fabrication Proceedings, 1995, pp 468-478

Abstract Computed tomography produces sets of tomograms for medical interpretation. Typical interpretation consists of imaging and simple observation on a 2-D display screen, so that feature extraction and tissue differentiation is based primarily on human expertise. Solid freefonn fabrication offers the promise of fabrication of prostheses based on actual patient anatomy. Use of CT data for this purpose requires automated interpretation. This paper presents a system architecture based on neural networks for the segmentation and classification of tissues of interest in tomograms. This approach produces a quantitative recovery of the available information by applying a feed-forward neural net trained with the back- propagation algorithm. The neural network architecture selected was tested on fabricated CT image matrices of the lower extremity. Index terms: Human anatomy, modeling; Prosthesis, manufacturing; Prosthesis, rapid prototyping; Tissue identification, neural networks (Auth abstract) [References: 5]

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The University of Texas at Austin
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Mechanical Engineering Dept.
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