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Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Progressive refinement of 3-d images using coded binary trees: algorithms and architecture.

D M Hardas1, S N Srihari

  • 1Department of Computer Science, State University of New York at Buffalo, Buffalo, NY 14260.; Tektronix Inc., Beaverton, OR 97077.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 14, 2012
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Summary
This summary is machine-generated.

This study introduces a novel encoding/decoding method and computer architecture for progressive refinement of 3-D images. This technique enables finer image resolution as more data is processed, optimizing 3-D image representation.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computer Architecture

Background:

  • Progressive refinement of 3-D images is crucial for efficient data handling and visualization.
  • Existing methods may lack efficiency in terms of processing time and space complexity.
  • VLSI implementation requires specialized architectures for high-performance computing.

Purpose of the Study:

  • To propose an efficient encoding/decoding technique for progressive refinement of 3-D images.
  • To introduce a computer architecture optimized for this encoding/decoding method.
  • To demonstrate the feasibility of VLSI implementation for the proposed architecture.

Main Methods:

  • A binary tree representation of grey-level images is utilized for encoding.
  • A scheme is presented to transform an N x N x N image array into a sequence of elements.
  • The computer architecture is designed for efficient transformations between image data and its encoded form.

Main Results:

  • The method allows for obtaining finer resolution 3-D image representations by scanning more elements of the sequence.
  • The proposed architecture performs transformations using O(log N) processors.
  • Processing time and space complexity are proportional to the image size.

Conclusions:

  • The developed encoding/decoding technique and architecture support progressive refinement of 3-D images efficiently.
  • The architecture is suitable for VLSI implementation, offering significant performance benefits.
  • This approach enhances the handling and representation of large 3-D image datasets.