HMRF-EM-image: Implementation of the Hidden Markov Random Field Model and its Expectation-Maximization Algorithm
Quan Wang
TL;DR
This paper presents a MATLAB toolbox for 2D and 3D image segmentation based on the hidden Markov random field model and its EM algorithm, emphasizing edge preservation and reconfigurability.
Contribution
It introduces a flexible MATLAB toolbox implementing HMRF-EM for image segmentation, including edge-prior preservation and adaptability to different dimensions.
Findings
Effective 2D image segmentation with edge preservation
Reconfigurable framework for 3D image segmentation
Accessible MATLAB implementation for researchers
Abstract
In this project, we study the hidden Markov random field (HMRF) model and its expectation-maximization (EM) algorithm. We implement a MATLAB toolbox named HMRF-EM-image for 2D image segmentation using the HMRF-EM framework. This toolbox also implements edge-prior-preserving image segmentation, and can be easily reconfigured for other problems, such as 3D image segmentation.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMedical Image Segmentation Techniques · Image and Object Detection Techniques · AI in cancer detection