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Our objective is to develop and apply novel neural networks and blind signal processing algorithms and methods: Spatiotemporal decorrelation, Dynamic Independent Components Analysis (ICA), Multichannel Blind Deconvolution/Separation (MBD), Higher Order Statistics (HOS), Unsupervised Nonlinear Adaptive Filtering (UNAF) and Time-Frequency (TF) methods to analyze multisensory biomedical signals especially EKG/MEG, EMG data.
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ICA, BSS and BSE Package for Images
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The ICALAB package for image processing developed in Advanced Brain Signal Processing Laboratory, BSI RIKEN with collaboration with our colleagues from associated laboratories.
On the left side there is a main window of the ICALAB and on the left input window allowing import of images in different formats.
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Three windows with 21 input original images (sources), than 21 rectangular parts taken for learning and 21 separated components in positive and negative versions.
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Components before and after deflation
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Using deflation approach we can decide which components contain information considered as noise. I the above example we decided to reconstruct the PET sequence only from components #3 and #5.
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And here is the result after deflation. The movie generated from the original images and after above deflation you can see below. We used more contrast color map for the movies.
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The original sequence of PET images before applying noise separation.
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The original sequence of PET images after applying noise separation.
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The PET data obtained in collaboration with Department of Physics, Ewha Womans University and Department of Nuclear Medicine,
Seoul National University Hospital, Korea.
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BSI RIKEN ABSP Lab.
2-1, HIROSAWA, WAKO-SHI,
SAITAMA, 351-0198
JAPAN
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