Paper Info
Title
Non Negative Matrix Factorization For Time Series Of Medical Images Analysis
Abstract
The application of Non Negative Matrix Factorization to time series of medical images analyze is investigated in this paper Time series images of the urinary system are acquired by intravenous pyelography (IVP). Factorial Analysis (FA) and Principal Component Analysis (PCA) are often used to extract time signatures or factors and associated compartments of factor images. Blind source separation methods such as Independent Component Analysis (ICA) may be an alternative to these methods for which the orthogonality condition is replaced with a more general constraint: the independence. Since the positivity constraint must not be ignored, we focused on the Non Negative Matrix Factorisation approach. More than that, there are situations where only few units of either factor loads and/or factor images are effectively used to represent observed data vectors. In this case, sparseness constraint must be taken into account.
Year
DOI
Venue
2009
10.1109/CISIS.2009.193
CISIS: 2009 INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS, VOLS 1 AND 2
Keywords
Field
DocType
sparse matrices,biomedical imaging,hyperspectral sensors,blind source separation,pixel,urinary system,independent component analysis,principal component analysis,time series,matrix decomposition,time series analysis,non negative matrix factorization,hyperspectral imaging,image analysis,feature extraction,artificial neural networks
Time series,Pattern recognition,Computer science,Matrix decomposition,Orthogonality,Independent component analysis,Non-negative matrix factorization,Artificial intelligence,Blind signal separation,Principal component analysis,Sparse matrix
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Cosmin Lazar11719.89
Danielle Nuzillard2185.81
Doncescu, A.38625.70