Signal extraction from movies of honeybee brain activity: the ImageBee plugin for KNIME

BACKGROUND: In the antennal lobe, a dedicated olfactory center of the honeybee brain, odours are encoded as activity patterns of coding units, the so-called glomeruli. Optical imaging with calcium-sensitive dyes allows us to record these activity patterns and to gain insight into olfactory information processing in the brain. METHOD: We introduce ImageBee, a plugin for the data analysis platform KNIME. ImageBee provides a variety of tools for processing optical imaging data. The main algorithm behind ImageBee is a matrix factorisation approach. Motivated by a data-specific, non-negative mixture model, the algorithm aims to select the generating extreme vectors of a convex cone that contains the data. It approximates the movie matrix by non-negative combinations of the extreme vectors. These correspond to pure glomerular signals that are not mixed with neighbour signals. RESULTS: Evaluation shows that the proposed algorithm can identify the relevant biological signals on imaging data from the honeybee AL, as well as it can recover implanted source signals from artificial data. CONCLUSIONS: ImageBee enables automated data processing and visualisation for optical imaging data from the insect AL. The modular implementation for KNIME offers a flexible platform for data analysis projects, where modules can be rearranged or added depending on the particular application. AVAILABILITY: ImageBee can be installed via the KNIME update service. Installation instructions are available at http://tech.knime.org/imagebee-analysing-imaging-data-from-the-honeybee-brain

Subspaces clustering approach to lossy image compression

Part 8: Pattern Recognition and Image ProcessingInternational audienceIn this contribution lossy image compression based on subspaces clustering is considered. Given a PCA factorization of each cluster into subspaces and a maximal compression error, we show that the selection of those subspaces that provide the optimal lossy image compression is equivalent to the 0-1 Knapsack Problem. We present a theoretical and an experimental comparison between accurate and approximate algorithms for solving the 0-1 Knapsack problem in the case of lossy image compression