Morphological bidirectional associative memories
Neural Networks
Reconstruction of Patterns from Noisy Inputs Using Morphological Associative Memories
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Morphological associative memories
IEEE Transactions on Neural Networks
FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices
Computers & Mathematics with Applications
High resolution segmentation of csf on phase contrast MRI
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Exploration of LICA detections in resting state fMRI
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Computers in Biology and Medicine
Lattice independent component analysis for mobile robot localization
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
A comparison of VBM results by SPM, ICA and LICA
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Hybrid computational methods for hyperspectral image analysis
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Fuzzy lattice reasoning for pattern classification using a new positive valuation function
Advances in Fuzzy Systems
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We introduce an approach to fMRI analysis based on the Endmember Induction Heuristic Algorithm (EIHA). This algorithm uses the Lattice Associative Memory (LAM) to detect Lattice Independent vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Induced endmembers are used to compute the activation levels of voxels as result of an unmixing process. The endmembers correspond to diverse activation patterns, one of these activation patterns corresponds to the resting state of the neuronal tissue. The on-line working of the algorithm does not need neither a previous training process nor a priori models of the data. Results on a case study compare with the results given by the state of art SPM software.