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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
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Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

Sparse texture active contour.

Yi Gao1, Sylvain Bouix, Martha Shenton

  • 1Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02215, USA. gaoyi@bwh.harvard.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel image segmentation method using sparse representation for complex textures. The approach efficiently models texture and improves region extraction in images.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Traditional image segmentation methods struggle with complex textured objects.
  • Existing techniques often rely on inadequate quantitative measures like mean intensity or gradient magnitude.
  • Sparse representation of natural signals offers a promising alternative for texture modeling.

Purpose of the Study:

  • To propose a non-parametric texture modeling technique for image segmentation.
  • To develop a segmentation scheme leveraging sparse representation of texture information.
  • To enhance the accuracy and efficiency of textural image segmentation.

Main Methods:

  • Texture is encoded using dictionaries constructed from user initialization.
  • An active contour model is evolved to optimize the fidelity of dictionary representation.
  • The proposed method utilizes the sparse characteristic of natural signals.

Main Results:

  • The developed algorithm demonstrates robust extraction of textured regions from images.
  • Experiments on diverse texture datasets validate the effectiveness of the approach.
  • User interaction and performance statistics were analyzed, showing computational efficiency.

Conclusions:

  • Sparse representation provides an effective non-parametric method for texture modeling in image segmentation.
  • The proposed active contour evolution optimizes texture representation fidelity.
  • The algorithm offers a computationally efficient and robust solution for segmenting textured image regions.