Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian image segmentation using local iso-intensity structural orientation.

Wilbur C K Wong1, Albert C S Chung

  • 1Lo Kwee-Seong Medical Image Laboratory and the Department of Computer Science, The Hong Kong University of Science and Technology, Kowloon, Hong Kong. cswilbur@cs.ust.hk

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|October 22, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Depth-Aware Networks for Multi-Organ Lesion Detection in Chest CT Scans.

Bioengineering (Basel, Switzerland)·2024
Same author

EfficientQ: An efficient and accurate post-training neural network quantization method for medical image segmentation.

Medical image analysis·2024
Same author

Retinal Vessel Segmentation by a Transformer-U-Net Hybrid Model With Dual-Path Decoder.

IEEE journal of biomedical and health informatics·2024
Same author

A Dual Coordinate System Vertebra Landmark Detection Network with Sparse-to-Dense Vertebral Line Interpolation.

Bioengineering (Basel, Switzerland)·2024
Same author

Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention.

IEEE transactions on medical imaging·2023

This study introduces a new image segmentation algorithm that relaxes the piecewise homogeneous assumption. It improves segmentation quality for non-uniform images by exploiting intensity profile coherence.

Area of Science:

  • Computer Vision
  • Image Processing
  • Medical Imaging

Background:

  • Image segmentation is crucial in computer vision, but traditional methods struggle with non-uniform, non-textured objects.
  • The piecewise homogeneous assumption, common in segmentation, is often invalid for real-world and medical images, leading to poor results.

Purpose of the Study:

  • To develop a novel image segmentation algorithm that overcomes limitations of existing methods.
  • To relax the piecewise homogeneous assumption by modeling smooth intensity non-uniformity.

Main Methods:

  • A Bayesian framework is employed for image segmentation.
  • A novel smoothness prior based on local structural orientation coherence is introduced.
  • Orientation tensors are used for local structural orientation estimation, outperforming Hessian matrices.

Related Experiment Videos

Main Results:

  • The proposed algorithm demonstrates superior performance in segmenting synthetic and real-world images.
  • Experimental results show improved segmentation quality compared to global thresholding and multilevel logistic MRF models.

Conclusions:

  • The novel algorithm effectively segments objects with smooth intensity non-uniformity.
  • Relaxing the piecewise homogeneous assumption and incorporating a coherence-based prior significantly enhances image segmentation accuracy.