Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Self-Organizing Maps
Compensation of Spatial Inhomogeneity in MRI Based on a Parametric Bias Estimate
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
Self-organizing maps with recursive neighborhood adaptation
Neural Networks - New developments in self-organizing maps
Segmentation of Multispectral MR Images Using a Hierarchical Self-Organizing Map
CBMS '01 Proceedings of the Fourteenth IEEE Symposium on Computer-Based Medical Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multispectral MR Images Segmentation Using SOM Network
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Pattern Recognition Letters
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A parametric gradient descent MRI intensity inhomogeneity correction algorithm
Pattern Recognition Letters
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
A Modified Probabilistic Neural Network for Partial Volume Segmentation in Brain MR Image
IEEE Transactions on Neural Networks
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
Bayesian segmentation of magnetic resonance images using the α-stable distribution
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Thrombus volume change visualization after endovascular abdominal aortic aneurysm repair
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Hybrid color space transformation to visualize color constancy
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - 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
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A widely accepted magnetic resonance imaging (MRI) model states that the observed voxel intensity is a piecewise constant signal intensity function corresponding to the tissue spatial distribution, corrupted with multiplicative and additive noise. The multiplicative noise is assumed to be a smooth bias field, it is called intensity inhomogeneity (IIH) field. Our approach to IIH correction is based on the definition of an energy function that incorporates some smoothness constraints into the conventional classification error function of the IIH corrected image. The IIH field estimation algorithm is a gradient descent of this energy function relative to the IIH field. We call it adaptive field rule (AFR). We comment on the likeness of our approach to the self-organizing map (SOM) learning rule, on the basis of the neighboring function that controls the influence of the neighborhood on each voxel's IIH estimation. We discuss the convergence properties of the algorithm. Experimental results show that AFR compares well with state of the art algorithms. Moreover, the mean signal intensity corresponding to each class of tissue can be estimated from the image data applying the gradient descent of the proposed energy function relative to the intensity class means. We test several variations of this gradient descent approach, which embody diverse assumptions about available a priori information.