Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)

Authors:
J. Xie;H. T. Tsui
Affiliations:
Department of Electronic Engineering, Computer Vision and Image Processing Laboratory, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Electronic Engineering, Computer Vision and Image Processing Laboratory, The Chinese University of Hong Kong, Shatin, Hong Kong
Venue:
Pattern Recognition Letters
Year:
2004

Citing 8
Cited 5

Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Relaxation on Partitions With Connected Components and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Flow: A Framework of Boundary Detection and Image Segmentation

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Color- and Texture-Based Image Segmentation Using EM and Its Application to Content-Based Image Retrieval

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing
Extensive partition operators, gray-level connected operators, and region merging/classification segmentation algorithms: theoretical links

IEEE Transactions on Image Processing

Automatic Tracking of Escherichia Coli Bacteria

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
MRI Brain image segmentation with supervised SOM and probability-based clustering method

IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Unsupervised neural techniques applied to MR brain image segmentation

Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies

Applied Soft Computing
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering

Information Sciences: an International Journal

Quantified Score

Hi-index	0.10

Visualization

Abstract

A novel method based on MLE-OED is proposed for unsupervised image segmentation of multiple objects with fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method is validated by experiments on both synthetic and real images. The experimental results show that the proposed method outperformed two popular methods.