CytoSpec - an APPLICATION FOR HYPERSPECTRAL IMAGING



 

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Spectral Preprocessing

Calculation of Derivative Spectra
Normalization (Vector, Offset)
Cut
Interpolate
Smooth
ABS <--> TR Conversion
Subtraction
Dispersion Correction
Quality Test
Baseline Correction
Water Vapor Compensation
Noise Correction
Cosmic Spike Removal
Batch Preprocessing

Spatial Preprocessing

Crop
Interpolate/Binning
Filter Images
3D-FSD

Univariate Imaging

Chemical Imaging
Chemical Movie
Frequency Imaging

Multivariate Imaging

HCA Imaging
KMC Imaging
FCM Cluster Imaging
PCA Imaging
VCA Imaging
n-findr Imaging
ANN Imaging
Synthon Imaging
Imaging with Distance Values

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PULL DOWN MENU "SPATIAL PREPROCESSING"

 

Spatial Preprocessing Menu
 

Crop Images


 
    CytoSpec's 'cut' subroutines offer two different methods to cut hyperspectral data sets:
       
    • Cutting in the spectral domain, and
    • Crop images in the spatial domains.

crop (spatial dimension)
 
Cropping in the spatial (x,y) dimensions reduces the number of pixel spectra. The number of pixel spectra can be defined by typing the spatial (pixel) coordinates of a rectangular region in the current map.  
Note that the 'cut/crop' function overwrites all existing data blocks (see also Internal Data Organization, Table IV).
 
The parameter used for 'cut' are stored within the program workspace and are accessible through the File Info pull down menu (File Info --> File Manipulations --> type of data block). These parameters are also shown in the command line window.
 

 

Interpolate in the Spatial Domain - Pixel Binning


 
    CytoSpec's 'interpolation' routines offers two different methods of interpolation:
       
    • interpolation in the spectral domain, and
    • interpolation in the spatial domains.

interpolation /pixel binning (spatial dimension)
 
Interpolation in the spatial (x,y) dimensions (pixel binning) changes the number of pixel spectra. The number of pixel spectra can be increased or decreased. For example, if the current number of pixel spectra is 64 x 64 ('# of pixel in x/y') and the settings for 'new xdim/ydim' are 32 x 32 the program performs a two-dimensional interpolation in the spatial domains decreasing the map size by a factor of 2 x 2. Note that map dimensions can be changed to any sizes, e.g. spatial interpolation from an initial map size 64 x 64 to a 30 x 32 pixel map will work. Note also that enlarging map sizes by spatial interpolation may result in very large data files.
 
Please uncheck the checkbox 'fix aspect ratio' if you wish to change the pixel ratio between x and y-dimensions.
The 'interpolate / pixel binning' function overwrites all existing data blocks (see also Internal Data Organization, Table III).
 
The parameter used for interpolate are stored within the program workspace and are accessible through File Info menu (File Info --> File Manipulations --> type of data block). These parameters are also shown in the command line window.
 

 

Filter Images

 
 
    The function 'filter images' is a function that can be used to apply frequency filters in the xy-image domain. Frequency filters such as low or high pass (smoothing/sharpening) filters are appplied in the image domain to smooth, or sharpen chemical images. The target data block of filtering in the image domain will be the data block of (de)convolution spectra if the function is applied to original, or pre-processed spectra. If derivative data are filtered, the target data block will be the block of derivative spectra (see also Internal Data Organization, Table IX).
     
filter image data
 
Select the source data block by clicking the appropriate radio button, then select the spatial filter function and the size of the kernel. To finally apply the spatial filter function click on the 'filter' button.
 
Available filters:
    • Gaussian
    • Mean
    • Disk
    • Savitzky-Golay smoothing
    • Laplacian
       
    •  
Kernel size: indicates the size of the kernel filter function in pixels
    The parameter used for 'filtering images' are stored within the program workspace and are accessible through the File Info pull down menu (File Info --> File Manipulations --> type of data block). These parameters are also shown in the command line window.

 

3D Fourier Self-Deconvolution (3D-FSD)


 
3D deconvolution

    Example: This example shows the results of 3D-FSD (data acquired by the use of a 64 x 64 mid-infrared MCT focal plane array detector). For chemical imaging, the absorbance values at 1731 wavenumbers of the original spectral (upper plot) and of the FSD data block (lower panel) were color encoded and plotted as a function of (x,y) position. The panel to the left displays the original (blue) spectra and FSD spectra (red).
     
    Important :
    3D Fourier self-deconvolution can be performed only on the original data (data block 1). This function is only suited for envelopes much broader than the spatial/spectral resolution. Avoid oscillatory patterns due to over-deconvolution!
     
    Reference to the literature:
     

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