Advanced algorithmic approaches to medical image segmentation
A General Probabilistic Formulation for Supervised Neural Classifiers
Journal of VLSI Signal Processing Systems
Parallelized segmentation of a serially sectioned whole human brain
Image and Vision Computing
Computer Methods and Programs in Biomedicine
Image segmentation using histogram fitting and spatial information
MDA'06/07 Proceedings of the 2007 international conference on Advances in mass data analysis of signals and images in medicine biotechnology and chemistry
Computers in Biology and Medicine
A robust statistical method for brain magnetic resonance image segmentation
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Structural and Multidisciplinary Optimization
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
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This paper presents a probabilistic neural network based technique for unsupervised quantification and segmentation of brain tissues from magnetic resonance images. It is shown that this problem can be solved by distribution learning and relaxation labeling, resulting in an efficient method that may be particularly useful in quantifying and segmenting abnormal brain tissues where the number of tissue types is unknown and the distributions of tissue types heavily overlap. The new technique uses suitable statistical models for both the pixel and context images and formulates the problem in terms of model-histogram fitting and global consistency labeling. The quantification is achieved by probabilistic self-organizing mixtures and the segmentation by a probabilistic constraint relaxation network. The experimental results show the efficient and robust performance of the new algorithm and that it outperforms the conventional classification based approaches