Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
A deterministic annealing approach to clustering
Pattern Recognition Letters
Topology representing networks
Neural Networks
GTM: the generative topographic mapping
Neural Computation
Segmentation with neural networks
Handbook of medical imaging
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Asymptotic level density in topological feature maps
IEEE Transactions on Neural Networks
Model-free functional MRI analysis based on unsupervised clustering
Journal of Biomedical Informatics
Fully automated biomedical image segmentation by self-organized model adaptation
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Image fusion based on topographic mappings using the hyperbolic space
Information Visualization
Clustering of dependent components: a new paradigm for fMRI signal detection
EURASIP Journal on Applied Signal Processing
Engineering Applications of Artificial Intelligence
ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Engineering Applications of Artificial Intelligence
The Exploration Machine --- A Novel Method for Data Visualization
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Clustering of time series data-a survey
Pattern Recognition
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Study and application of medical image visualization technology
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Information-theoretic model selection for independent components
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Computer Science - Research and Development
Journal of Biomedical Imaging
A novel clustering method on time series data
Expert Systems with Applications: An International Journal
Clustering of machining signal for verifying machining parameter
SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
Exploiting HPC resources for the 3D-time series analysis of caries lesion activity
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
Shape-Based clustering for time series data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Measuring non-gaussianity by phi-transformed and fuzzy histograms
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
XSEDE-enabled high-throughput lesion activity assessment
Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery
| Hi-index | 0.00 |
In this paper, we present neural network clustering by deterministic annealing as a powerful strategy for self-organized segmentation of biomedical image time-series data identifying groups of pixels sharing common properties of local signal dynamics. After introducing the theoretical concept of minimal free energy vector quantization and related clustering techniques, we discuss its potential to serve as a multi-purpose computer vision strategy to image time-series analysis and visualization for many fields of medicine ranging from biomedical basic research to clinical assessment of patient data. In particular, we present applications to (i) functional MRI data analysis for human brain mapping, (ii) dynamic contrast-enhanced perfusion MRI for the diagnosis of cerebrovascular disease, and (iii) magnetic resonance mammography for the analysis of suspicious lesions in patients with breast cancer. This wide scope of completely different medical applications illustrates the flexibility and conceptual power of neural network vector quantization in this context. Although there are obvious methodological similarities, each application requires specific careful consideration w.r.t. data preprocessing, postprocessing and interpretation. This challenge can only be managed by close interdisciplinary cooperation of medical doctors, engineers, and computer scientists. Hence, this field of research can serve as an example for lively cross-fertilization between computer vision and related research.