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Related Experiment Video

Updated: Jul 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Entropy-controlled quadratic markov measure field models for efficient image segmentation.

Mariano Rivera1, Omar Ocegueda, Jose L Marroquin

  • 1Department of Computer Science, Centro de Investigacion en Matematicas A.C., Guanajuato, Gto. 36000, Mexico. mrivera@cimat.mx

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|December 21, 2007
PubMed
Summary
This summary is machine-generated.

We developed a novel Markov random field (MRF) model for parametric image segmentation. This approach computes pixel probabilities, offering flexibility in estimators beyond the mode for enhanced segmentation accuracy.

Related Experiment Videos

Last Updated: Jul 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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Area of Science:

  • Computer Vision
  • Medical Image Analysis
  • Machine Learning

Background:

  • Parametric image segmentation is crucial for analyzing medical images like MRI.
  • Existing methods often directly compute label maps, limiting flexibility.
  • Incorporating prior knowledge like smoothness is essential for accurate segmentation.

Purpose of the Study:

  • To introduce a new Markov random field (MRF) based model for parametric image segmentation.
  • To compute pixel-wise probabilities of data generation by intensity models.
  • To offer a flexible framework adaptable to various estimators and applications.

Main Methods:

  • Utilized a Markov random field (MRF) with quadratic potentials to encode prior information.
  • Formulated segmentation as solving a quadratic cost function with linear constraints.
  • Computed the probability of observed data at each pixel being generated by an intensity model.

Main Results:

  • The proposed MRF model effectively performs parametric image segmentation.
  • Demonstrated flexibility by allowing the use of mean or median estimators alongside the mode.
  • Validated through numerical experiments on synthetic and real brain MRI data.

Conclusions:

  • The novel MRF model provides a robust and flexible approach to parametric image segmentation.
  • The methodology shows potential for extension to related problems like stereo disparity estimation.
  • The method's performance was confirmed through comparisons with existing techniques on real-world medical imaging data.