Pattern Recognition
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An analysis of histogram-based thresholding algorithms
CVGIP: Graphical Models and Image Processing
Curve fitting by a sum of Gaussians
CVGIP: Graphical Models and Image Processing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Goal-Directed Evaluation of Binarization Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Analysis by Accurate Identification of Simple Markovian Models
Cybernetics and Systems Analysis
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A new algorithm for segmenting a multimodal grey-scale image is proposed. The image is described as a sample of a joint Gibbs random field of region labels and grey levels. To initialize the model, a mixed multimodal empirical grey-level distribution is approximated with linear combinations of Gaussians, one combination per region. Bayesian decisions involving expectation maximization and genetic optimization techniques are used to sequentially estimate and refine parameters of the model, including the number of Gaussians for each region. The final estimates are more accurate than with conventional normal mixture models and result in more adequate region borders in the image. Experiments show that the proposed technique segments complex multimodal medical images of different types more accurately than several other known algorithms.