Image smoothing using non-linear filters a comparative study

2014 ◽  
Author(s):  
Melika Mostaghim ◽  
Elnaz Ghodousi ◽  
Farshad Tajeripoor
Keyword(s):  
Comparative Study ◽  
Image Smoothing ◽  
Linear Filters ◽  
Non Linear

scholarly journals SPATIO-TEMPORAL DATA ANALYSIS WITH NON-LINEAR FILTERS: BRAIN MAPPING WITH fMRI DATA

2011 ◽  
Vol 19 (3) ◽  
pp. 189
Author(s):  
Karsten Rodenacker ◽  
Klaus Hahn ◽  
Gerhard Winkler ◽  
Dorothea P Auer
Keyword(s):  
Signal To Noise Ratio ◽  
Three Dimensional ◽  
Digital Data ◽  
Linear Filters ◽  
Brain Functions ◽  
Non Linear ◽  
Long Time ◽  
Spatio Temporal ◽  
Sensory Activation ◽  
Temporal Data Analysis

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

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