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 "SPECTRAL PREPROCESSING"

 

Spectral Preprocessing Menu
 

Savitzky-Golay Derivatives


 
take derivative
    Derivative calculation is carried out by applying the Savitzky-Golay algorithm. In this method n-th order derivatives are obtained while data are smoothed at the same time to reduce the noise. First or second order derivatives can be calculated including 5 to 25 smoothing points. Please note that derivatives are taken in the spectral domain, only. Details of the Savitzky-Golay algorithm can be found in the literature:
     
    A. Savitzky and M. Golay. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964 Vol 36(8):1627.
     
    Any type of data blocks can be handled (including also derivatives). Derivative spectra are stored in a data block reserved exclusively for derivative spectra. If this block is not empty the data are overwritten without warning when obtaining derivatives again (see also Internal Data Organization, Table II )). Derivative calculation is always carried out on one complete 3D spectral data block.
     
    Select the source data block by clicking the appropriate radio button, then select the number of smoothing points and the order of the derivative. To finally obtain the derivatives click on the 'derive' button.
     
    Parameter used for obtaining derivative spectra can be stored within the program workspace and are accessible in the File Info menu (File Info --> File Manipulations --> derivatives). These parameters are also shown in the command line window.
     

 

 

Normalization


 
normalize spectra
    The CytoSpec 'normalization' subroutine offers three different methods of spectra normalization:
     
      1. Offset correction
      2. Min-Max normalization
      3. Vector normalization
      4. SNV (standard normal variate)
       
    Offset correction performs are linear correction of the complete spectrum such that at least on point of the spectral region indicated equals zero. Spectra are not scaled in this mode.
     
    Min-Max normalization indicates that all spectra of the source data block are scaled between zero and one, that is the maximal absorbance value of the spectrum in the selected spectral region equals one, the minimum 0. You can use this normalization method to perform a simple band normalization (e.g. for the amide I band).
     
    Vector normalization is carried out in the following way: spectra are first mean-centred, i.e. the average value of the absorbances is calculated for the spectral region indicated. This value is then subtracted from the spectrum. Then, the spectra are scaled such, that the sum squared deviation over the indicated wavelengths equals one.
     
    Standard Normal Variate (SNV) A standard normal variate is a normal variate with mean =0 and standard deviation sigma=1. SNV normalization is achieved by dividing mean-centred spectra by the standard deviation over the spectral intensities giving the resulting spectra a unit standard deviation of one.
     

     
    To perform normalization select first the source data block by activating the appropriate radio button (see also Internal Data Organization, Table I ). The target data block will be specified in the gray field of the normalization window. Please note that the data of the target data block will be overwritten without warning! Then, select the type of normalization and use the keyboard to enter the wavenumber values between which the spectra the spectra will be normalized. To start normalization click onto the 'norm' button. If you wish to cancel the operation press 'cancel'.
     
    Parameters used for normalization such as spectral range are stored within the program workspace and are accessible through the File Info menu (File Info --> File Manipulations --> type of data block). These parameters are also shown in the command line window.
     

 

Cut Spectra


 
    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.

cut (spectral dimension)
 
Cutting in the spectral (z)- dimension can be used to narrow the frequency range of spectral data files. This may be useful to free some memory before memory-consuming calculations such as 3D Fourier self deconvolution are carried out. Define the frequency range to be kept, then click on the 'cut' button to start the function. By pressing the 'cancel' button you can cancel the operation.
 
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.
 

 

Interpolation (Spectral Domain)


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

interpolation (spectral dimension)
 
Interpolation in the spectral (z)- dimension changes the spacing between spectral data points. The spacing can be increased or decreased by the 'interpolation factor', which can vary between 1/32 and 32. For example, if a factor of 4 is chosen, the number of data points is increased by a factor of 4, i.e. one frequency interval is filled with (4-1) additional data points. In this case the program performs an one-dimensional interpolation of the spectra. Using a large interpolation factor (e.g. 32) the number of data points of the new spectrum may become rather large. The actual number of data points depends on the start and end frequency and the frequency interval of the original spectrum.
 
If a factor smaller than 1 is chosen, the data point spacing is decreased. For example, if a factor of 0.25 is chosen, the number of data points is decreased by a factor of 4, i.e. four frequency intervals are merged into one wavelength interval. Consequently, spectral information is lost. Interpolation is useful to reduce the noise or to free some memory before memory-consuming calculations such as 3D-Fourier self deconvolution (3D-FSD) are carried out.
 
The 'interpolate' 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.
 

 

Smoothing


 
smoothing

 

    Smoothing: This function is used to smooth spectra, using either the Savitzky-Golay, or the average smoothing algorithm. Possible values for smoothing points are 5 to 25. Select the source data block as usual, choose the number of smoothing points and click the 'smooth' button to start the operation. Smoothing has a mostly cosmetic effect on the spectrum, reducing the noise at the expense of distorting the signals.
     
    Details of the Savitzky-Golay algorithm can be found in the literature:
    A. Savitzky and M. Golay. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 1964 Vol 36(8):1627.
     
    The parameters used for smoothing 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.
     

 

Conversion Absorbance <--> Transmission


 

    TR <--> ABS conversion: This function performs the conversion from transmission spectra to absorbance spectra and vice versa. Note: The function ABS <--> TR acts on the complete data block of original spectra and overwrites the data block of original spectra. Furthermore, all other types of data will be deleted.
     
    For the conversion of absorbance spectra to transmission spectra the following formula is used:
     
    TR --> ABS

     
    Formula, used to obtain absorbance spectra from transmission spectra:
     
    ABS --> TR

     

Subtraction


 

    Subtraction: This function permits subtraction of spectra from complete spectral data blocks. The function might be useful to compensate for spectral contributions of supporting substrates in transmission type imaging data (example: subtraction of absorbance spectra of thin films).
     
    Spectral subtraction can be carried in two ways: by an internal, or an external spectrum:
     
    • Internal spectrum - a spectrum that is contained in the actual spectral map.
    • External spectrum - the spectrum can be loaded (ASCII format).

    •  
    subtraction

     
    Using external spectrum for subtraction: in order to use this function check the appropriate radiobutton. Load the external ASCII spectrum (details of data format are given below). Type in the scaling factor and select the source data block. After pressing the 'subtract' button the external spectrum will be multiplied by the scaling factor and the resulting spectrum is subsequently subtracted from all spectra of the source data block. .
     
    Using internal spectrum for subtraction: check the radiobutton 'use internal spectrum' and choose the (x,y) pixel positions (coordinates) of the spectrum you wish to subtract from the map. Note that upon its initialization the 'subtract' window will read the actual (x,y) coordinates from the main gui. Press the 'subtract' button after selecting the source data block.
     
    Source block: Here you can choose the type of the source block for the subtraction function. Please note that 3D-FSD data cannot be used as source data.
     
    Target block: If the source data blocks are of the type original or preprocessed, the target data block will be of the type of preprocessed data. If the source data are of the type derivative the target data block will be also of this type (existing data are overwritten without warning, see also Internal Data Organization). The spectral subtraction routine is always carried out on the complete 3D spectral data block.
     
    Button load spectrum: Permits to load a double column ASCII spectrum. If the file could be successfully loaded the directory and the file name is displayed and the button 'subtract' becomes activated.
     
    Button subtract: Starts the subtraction routine immediately.
     
    Button cancel: The routine is aborted.
     
    Note: In order to be able to subtract an external spectrum one have first to produce a double column ASCII spectrum (for details of the data format see spectra vap_cut.dat or wap_full.dat; both spectra can be found in the directory CytoSpecRootDir/Testdata/watervap/). Upon loading the external spectrum is automatically adapted such that its data point spacing and its frequency range fits that of the sample data:
    It will be interpolated (alternative point spacing), cut (broader frequency range), and/or extrapolated (narrower range). Extrapolation is achieved by using the closest absorbance value to fill missing data points.
     

Dispersion Correction


 

    Dispersion correction: This function performs the correction of dispersion artefacts from transmittance spectra (function written by Dr. Melissa Romeo).
     
    The correction of dispersion artefacts is carried out in the following way:
     
    1. Transmittance spectra are linearly interpolated such that they contain 2^n data points (256, 512, ... , 16384)
    2. These spectra are then Fourier-transformed.
    3. The first half of the Fourier transformed function is inversely Fourier-transformed. This operation produces a complex function containing a real and an imaginary part.
    4. Negative terms in the imaginary component are compensated.
    5. Squares of the real and imaginary terms are taken.
    6. The squared terms are co-added.
    7. The root of the sum yields a phase-corrected spectrum.
    8. Transmittance spectra are back-interpolated and converted into absorbance spectra. They are stored in the data block of preprocessed data.

Quality Tests


     
    Quality Test: The function 'quality test' implemented in the CytoSpec software comprises five distinct checks for spectral quality:
     
    1. a test for spectral signs of water vapor
    2. the check for sample thickness (integrated intensity)
    3. the test of the spectral signal-to-noise-ratio
    4. a check called 'test for an additional band'
    5. a 'bad pixel' test (a tool to eliminate spectra from dead pixels of focal plane array detectors

    6.  
    Data organisation: the quality tests are performed exclusively on the data block of original spectra. Spectra that have passed the tests are copied without modifications into the data block of preprocessed spectra. Note that existing data of this block are overwritten without warning. If the quality test of a given spectrum is negative, the respective field in the preprocessed data block is replaced by NaN (Not a Number). In this way, spectra tested for poor quality are excluded from further evaluations and will appear in the hyperspectral images as black areas.
     
    If you wish to perform a quality test on preprocessed spectra, for example a sample thickness test after baseline correction, you have to use the Swap Data Block function of the 'tools' pull down menu. This function enables you to overwrite the data block of original spectra by preprocessed spectra.
     
    To enable a test, check the appropriate checkbox and specify the quality test parameters such as absorbance thresholds. Press the 'test' button to start the quality test or hit 'cancel' if the test should be canceled. The parameters of the test for spectral quality and details of the test results can be found in the File Info menu (File Info --> File Manipulations --> preprocessed). These parameters are also displayed in the command line window.
     
Quality tests

 
    1. Test for water vapor:
    Sharp water vapor absorption bands can be found in the spectral region between 1300 and 1800 1/cm, a region where many biomaterials exhibit also strong absorption bands. It is therefore recommended to use water vapor bands above 1750 1/cm for testing. Indicate the precise positions of two water vapor bands which should be utilized for testing and define an absorption threshold criterion. If the absorption of one of the bands is higher than the specified criterion, the test result for the given spectrum will be negative, and the spectrum will be eliminated.
     
    2. Integral absorption as a measure for sample thickness:
    The absorbance, integrated over a large spectral region, can be used as a rough measure of sample thickness in transmission type measurements. As many multivariate imaging techniques such as HCA or ANN imaging require a consistent level of the SNR throughout the map, spectra with too low absorptions have to be excluded from further multivariate analysis. On the other hand you may want to eliminate also spectra showing intense signals. This could be the case where the Beer-Lambert law is not obeyed (total absorption, non-linear detector response, etc.)
    In order to apply the 'sample thickness' criterion indicate the spectral region to be used for obtaining the integral. Next, define a upper and a lower threshold for the integral (edit field lower/upper limit). Check the appropriate checkbox to enable the test. A spectrum has failed the sample thickness test if an integration value is determined which is higher or lower than the defined thresholds.
     
    3. Signal/noise ratio (SNR):
    This test allows the signal-noise-ratio for individual spectra to be calculated, and to eliminate those that do not fulfill a threshold SNR ratio. Indicate the spectral regions to be used for defining the noise and signal, respectively. For biomedical samples, it is recommended to obtain the signal in the amide I region (1600 - 1700 1/cm) and the noise in the region between 1800-1900 1/cm. Also indicate the SNR threshold and check the checkbox for the SNR test. Spectra are rejected if the SNR is lower than the threshold.
     
      Noise: the standard deviation in the defined spectral range:
       
      Quality test

       
      Signal: the maximum ordinate value in the defined wavenumber range

     
    4. Test for an additional band:
    This test is useful to exclude spectra from the data set that contain an artifact band (example: regions of a tissue section contaminated by tissue embedding medium). Indicate a typical band position (carbonyl esters of tissue freezing medium: 1746 1/cm) and an absorbance threshold (edit field criterion). Spectra with a higher absorbance at this frequency will be eliminated.
     
    5. Elimination of 'bad' pixel from FPA data:
    Most of the focal plane array (FPA) detectors have so-called 'dead pixels', i.e. detector elements with zero response to IR radiation. The spectral information at these FPA elements is usually replaced by the camera software with interpolated data from pixel neighbors. If you wish to remove interpolated spectra from the data set, you have to create a simple text file, which should contain the dead pixel (x,y) positions. The text file can be loaded by activating the appropriate check box. Spectra at the given positions are then replaced by NaNs (not a number), i.e. excluded from all subsequent calculations.
     
    Please note: Please use the function Define Spectral Regions to define sample areas in which spectra should be excluded from further analyses.

Spectral Baseline Correction

Subtract linear baseline
    source block: please select a data block you wish to correct (normalize) by the linear baseline (offset) correction routine.
     
    spectral region: the spectral region, in which the baseline function is searching for a minimum y-value of the spectrum which is subtracted from the spectrum.
     
    norm: clicking on the 'norm' button corrects the baseline.
     
    cancel: closes the application.
     
Savitzky-Golay baseline correction
    source block: please select a data block you wish to compensate for non-linear baseline effects.
     
    number of smoothing points: number of smoothing points used for Savitzky-Golay smoothing.
     
    interpolate spectral region: a spectral region, in which the slope of the baseline should be interpolated. To activate this feature you have to check the appropriate checkbox and to indicate the wavenumber values of the spectral region you wish to exclude from baseline calculation (in biomedical spectroscopy, this may be the amide I and II region: 1520-1700 1/cm).
     
    correct: the baseline correction procedure is initiated.
     
    cancel: closes the application.
     

     
    Example: The figure below exemplary illustrates how the algorithm of Savitzky-Golay baseline correction works.
     
      red spectra: original FT-IR absorbance spectra.
       
      blue spectra: baseline curves as obtained by integration of extensively smoothed spectra . The left part of the figure shows baselines obtained with 99 smoothing points and the right panel with 249 points (resolution in the original spectra: 8 1/cm; zero-filling-factor of 4; data point spacing: 2). The example to the right demonstrates additionally the effect of the option 'interpolate region' which was used to interpolate the baseline in the amide I and II regions (1520 - 1700 1/cm).
       
      black spectra: red (original) minus blue (baseline) spectra. These spectra are stored in the data block of preprocessed spectra.
       
      Note: due to the of Savitzky-Golay algorithm, baseline correction might be ineffective in regions close to the upper and lower wavenumber limits (UWN, LWN), particularly if a high number of smoothing points have been chosen. If the number of smoothing points is NOP and the data point spacing is DPS, the baseline correction routine will perform a linear extrapolation of the baseline in the spectral regions
                [UWN] - [UWN-(NOP-1)/2*DPS] and
                [LWN] + [LWN-(NOP-1)/2*DPS].
example 1example 2
baseline from minima
    source block: please select a data block you wish to compensate for non-linear baseline effects. Note that this function does not work on derivative or 3D FSD data.
     
    number of intervals: number of intervals in which the spectrum is divided.
     
    interpolate spectral region: a spectral region, in which the algorithm should not search for baseline points. If this option is activated you are able to enter the wavenumber values of this spectral region.
     
    correct: the baseline correction procedure is initiated.
     
    cancel: closes the application.
     

Water Vapor Correction


     
    Water vapor compensation: This function permits to automatically subtract a water vapor spectrum from the measurement data such that the spectral effects of water vapor are minimized.
     
    The water vapor correction routine works as follows:
     
    1. A second derivative spectrum of a pure water vapor absorbance spectrum is obtained.
    2. Then, a second derivative spectrum is calculated from the sample spectrum.
    3. Depending on your selection, up to 4 separate y-values at defined spectral positions are obtained for both derivative spectra.
    4. The water vapor correction factor is calculated by dividing the respective y-values of the water vapor and the sample spectrum. If more than one y-value was selected, the final water correction factor is the average of the ratios.
    5. Finally, the sample data are corrected by subtracting the original water vapor spectrum, which was weighted by the water vapor correction factor.

water vapor correction

 
    water vapor correction of derivative spectra: If you wish to perform water vapor compensation on derivative spectra, you have to make sure that spectra are 2nd derivative spectra and that derivative calculations are carried out by choosing 5 smoothing points in the Savitzky-Golay algorithm. The algorithm described above will not work if these two preconditions are not fulfilled.
     
    number of vapor bands: Please choose the number of water vapor bands on which the spectral compensation for water vapor bands should be carried out.
     
    edit fields 1-4: Enter the correct positions (in wavenumbers) of water vapor bands. Please note that the band positions may slightly differ from instrument to instrument (calibration) and also as a function of the temperature.
     
    Source block: Here you can choose the type of the source block for water vapor compensation.
     
    load vapor file: Permits to load a double column ASCII water vapor spectrum. If the file could be successfully loaded the directory and the file name are displayed and the button 'correct' becomes activated.
     
    correct: Starts the spectral water vapor correction routine.
     
    cancel: The routine is aborted.
     
    data organization (source and target data blocks): Any type of data blocks (except 3D-FSD data) can be handled (including also derivatives). If the source block is of type of original spectra, or preprocessed spectra, the data are stored in the data block of preprocessed spectra. If this block is not empty the data are overwritten. Water vapor compensated derivative spectra are stored in the data block of derivative spectra (existing data are also overwritten without warning, see also Internal Data Organization, Table II). The water compensation is always carried out on the complete 3D spectral data block.
     
    note: In order to spectrally compensate for water vapor one have first to produce a double column ASCII spectrum of water vapor (for details of the data format see spectra vap_cut.dat or wap_full.dat; both spectra can be found in the directory CytoSpecRootDir/Testdata/watervap/.
     
    Upon loading the external spectrum is automatically adapted such that its data point spacing and its frequency range fits that of the sample data:
    • It will be interpolated (if the point spacing is different), cut (broader frequency range), and/or extrapolated (narrower range).
    • Extrapolation is achieved by using the closest absorbance value to fill missing data points.
     
    In the water vapor testdata directory (CytoSpecRootDir/Testdata/watervap/) you can find a test file named 'watervap.mat'. The first data block of this file (original data) contains the original absorbance spectra. Water vapor corrected IR absorbance spectra are found in the second data block of preprocessed spectra. Original spectra are corrected by using the file 'vap_full.dat'.

PCA Based Noise Reduction


 

    PCA based noise reduction: This function can be used to reduce spectral noise. Noise is eliminated by performing principal component analysis (PCA) of the image data and re-assembling of spectra on the basis of a selection of principal components (low order PCs) . In this way, higher-order principal components that are supposed to contain mainly 'noise' are omitted.
     
    PCA based noise reduction can be carried out on the basis of original or preprocessed data sets. The target data block will be always the data block of preprocessed data.
     
    PCA based noise reduction

     
    Important: Please carefully use this preprocessing routine! The decision which of the PCs can be omitted is highly subjective and may cause spectral artifacts.
     
    The algorithm has been adapted from a suggestion of Dr. Spragg R. (PerkinElmer) "Addressing Problems in Data Reduction for FT-IR Images of Biological Samples" (Oral Contribution). RISBM - Raman and IR spectroscopy in Biological Medicine. Feb 29-Mar 02, 2004. Friedrich-Schiller-University, Jena, Germany.
     

Cosmic Spike Correction


 

    Cosmic spike removal: The cosmic spike removal tool allows the user to remove cosmic ray features from the spectral (Raman) data. The function can be chosen from the preprocessing pulldown menu. When this function is activated a dialog box shows up which allows the user to change parameters of the cosmic spike removal filter.
     
    With this filter the removal of cosmic spikes is carried out in the following way:
     
    1. Smoothing of the spectral data of choice (original or preprocessed) in the spectral dimension by a 7-point Savitzky-Golay algorithm.

    2. A 3D-array of difference values between the un-smoothed and smoothed data is obtained.

    3. This array of difference values is normalized by dividing it by the mean standard deviation of the complete array.

    4. Cosmic spikes are now obtained by a systematic analysis in the spatial domains. For this purpose, maxima are obtained in each of the image planes of the normalized array of spectral differences. In this context, the parameter 'sensitivity' of the cosmic spike removal dialog box is used to define a threshold above which Raman intensity difference value supposedly indicate the presence of cosmic rays. The higher the sensitivity the lower this threshold.

    5. In the next step, the spatial coordinates of spectra with cosmic spike candidates are determined. Cosmic spikes are excised by replacing them with Raman intensities from neighboring frequencies (spectral domain). The parameter 'spikes width' of the dialog box defines the width of the excised spike in points.

    6. The cosmic spike removal tool can be applied to data block of original or pre-processed data. In both cases the spike-corrected Raman data are written into the data block of pre-processed spectra. Note that existing pre-processed data are overwritten without warning.

cosmic spike removal
    source block: please select the type of data block you wish to correct
     
    sensitivity: used to define a threshold above which Raman intensity difference values supposedly indicate cosmic spikes. The higher the sensitivity the lower the threshold.
     
    spikes width: defines the width of the excised spike in data points.
     
    verbose mode: displays more details of spike removal function.
     
    remove: starts the cosmic spike filter on the data block of choice.
     
    cancel: closes the dialog box.
     

Batch Preprocessing


 

    Batch preprocessing: This function permits automated preprocessing of spectral data. When this option is chosen one have to select a predefined macro file (*.cbt -CytoSpec batch) that should be generated before. You can use a plain text editor (Wordpad, etc.) for editing this file. Store your customized *.cbt file in a simple text format (no special characters or format tags!).
    CytoSpec's batch processing files contain blocks that start with one of the following three letter codes:
     
      DER - derivative
      NRM - normalize
      CUT - cut
      INT - interpolate
      SMO - smooth
      ATR - ABS --> TR conversion
      TRA - TR --> ABS conversion
      QAL - quality test
      BAS - baseline correction
      WVC - water vapor correction
      SWA - swap data blocks
      CSR - cosmic ray removal
     
    Each of the block contains a number of parameters required for processing the imaging data (such as type of datablock, wavenumber values for normalization etc.). These parameters start with a three letter code followed by space character and a numeric value. Details are explained in the example below. Note also that the blocks should be terminated by a 'END' line.
     
    The sequence of preprocessing steps is given by the sequence in the batch file. If you wish to omit preprocessing functions simply comment out the respective block by setting the '#' sign at the first position of a line. Please refer also to the online help or to the example file that comes with CytoSpec's installation CD.
     
    This is an example of the block 'CUT' in a CytoSpec batch (*.cbt) file:

    # --------------- CUT --------------------------------------------------
    CUT
    TYP 1    # type of cutting (1-spectral, 2 spatial dimension)
    WV1 1000 # first wavenumber for cut in spectral dimension
    WV2 1800 # last wavenumber (WV2 larger than WV2!)
    XD1 1    # cut, spatial dimension x : first pixel to keep
    XD2 10   # cut, spatial dimension x : last pixel to keep
    YD1 1    # cut, spatial dimension y : first pixel to keep
    YD2 10   # cut, spatial dimension y : last pixel to keep
    END
    # some lines with comments may follow
     
    next block ...

A detailed example of a CytoSpec batch file is given here: first.cbt


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