Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
“Brownian strings”: segmenting images with stochastically deformable contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A contour detection method: initialization and contour model
Pattern Recognition Letters
Image Parsing: Unifying Segmentation, Detection, and Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Regression based Bandwidth Selection for Segmentation using Parzen Windows
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to segment images using region-based perceptual features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means
Fundamenta Informaticae
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Automatic Unsupervised Segmentation Methods for MRI Based on Modified Fuzzy C-Means
Fundamenta Informaticae
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We propose new techniques for unsupervised segmentation of multi-modal grayscale images such that each region-of-interest relates to a single dominant mode of the empirical marginal probability distribution of gray levels. We follow most conventional approaches such that initial images and desired maps of regions are described by a joint Markov–Gibbs random field (MGRF) model of independent image signals and interdependent region labels. But our focus is on more accurate model identification. To better specify region borders, each empirical distribution of image signals is precisely approximated by a linear combination of Gaussians (LCG) with positive and negative components. Initial segmentation based on the LCG-models is then iteratively refined by using the MGRF with analytically estimated potentials. The convergence of the overall segmentation algorithm at each stage is discussed. Experiments with medical images show that the proposed segmentation is more accurate than other known alternatives.