Nisthresh

Performs background removal on a set of neuroimaging scans. For each image in the set, an intensity (or grey level) threshold is computed below which all voxels are considered to be part of the background, and consequently set to 0. The choice of the threshold is automatically performed by analyzing the grey level histogram of the image, as described below in the Algortihm description section.

Invocation

java Nisthresh

Nisthresh window

Information to be entered

• Input file/directory
  Text file with filenames of images to be thresholded, or the name of a directory with such images. This program will try to uncompress the images if they are compressed.
• Output directory
  Directory where the output images will be placed. This program will try to create this directory if it doesn't already exist.
• Write thresholds to file
  If checked, will write an ascii file with the specified name with all the thresholds values computed for each image of the set.
• Compress input images when finished
  Check this box if you want input images to be compressed (gzipped) after processing so as to save disk space.
• Options...
  Select other thresholding options
  • Algorithm parameters
    The five parameters of the algorithm are described in details in the following section (Algorithm description). It is recommended to use the default values which will lead in most of the cases to a meaningful choice of the threshold value. If one want to reduce (resp. increase) the number of remaining voxels in the brain, it is recommended to raise (resp. lower) the position parameter to a value of 0.6 or 0.7 (resp. 0.4 or 0.3).
  • Use output stem
    The default is to prepend a 't' to each input image filename in creating the filename of the output image, so that the thresholded version of "func_00427.img" would be named "tfunc_00427.img" (for example). Check this box if you want to specify the output stem instead (a stem of "thresh_" would result in an output filename of "thresh_00427.img" in this example). Checking this box will also force the output image sequence to start on 1 and increase by 1 for each subsequent image in the filelist, which is useful if the input images come from different directories but have some identical image stems and numbers, or if images have been inserted or deleted and a contiguous sequence is desired.
  • Write thresholded images to disk
    Unchecking this box will prevent the thresholded images from being written.
  • Verbose mode
    Checking this box will print details about each image as it is thresholded.

Algorithm description

All words in italic are internal variables of the algorithm. Words in bold are parameters which can be specified by the user through the options window of the Nisthresh application.

1. Compute the histogram of the image

The histogram is the curve associated with a grey-level image that shows the number of pixels in the image which have a certain grey level. We don't consider pixels with grey level 0 because they are useless in the algorithm. You can choose the number of grey levels you want per histogram bin by specifying the parameter greys. It has to be a positive integer. For example, if you put 3, the first point of the histogram will be the number of pixels which have grey level 1, 2 and 3, the second point will be the number of pixels which have grey levels 4, 5 and 6. The default is 2 for this parameter.

2. Smooth the histogram

The next step consists in smoothing the resulting curve so that the histogram is easier to process. This is controlled by parameter length, which allows the user to specify the half length for the smoothing. This must be a positive integer. For each point of the histogram, we compute the mean in a window of length 2*length+1 centered on the current point. The result is the smoothed histogram (which will be called 'histogram' in what follows). The default for this parameter is 5.

3. Search for the primary peak

The primary peak is simply given by the absolute maximum of the histogram. Its position is named pos_max1.

4. Search for the secondary peak

Then we look for the secondary peak. We find it by computing the maximum of a sub-part of the histogram, beginning a "certain amount away" from pos_max1. The starting point pos_start of the search is defined by the equation search = (pos_start - pos_max1) / (max_grey - pos_max1), where search must be a floating point number between 0 and 1. The default value for this parameter is 0.2. We get through this operation the position pos_max2 and value val_max2 of the secondary peak.

5. Search for the minimum between the two peaks

This operation is straightforward: pos_min is the position of the minimum value, then denoted as val_min, of the histogram between pos_max1 and pos_max2.

6. Find the limits of the range where we will choose the threshold

We define a cut value val_cut (a horizontal line on the histogram) by the equation cut = (val_cut - val_min) / (val_max2 - val_min), where cut must be a floating point number between 0 and 1. The default value for this parameter is 0.3. We now consider the intersecting points between this horizontal line at value val_cut and the histogram curve. And we define min_range as being the lowest position of these points above pos_max1, and max_range as being the highest position of these points below pos_max2.

7. Choose the threshold

We now choose the position of the threshold pos_thresh between min_range and max_range according to the value of the parameter position, which must be a floating point value between 0 and 1, defined by the equation position = (pos_thresh - min_range) / (max_range - min_range). The default value for this parameter is 0.5 (middle of the range).

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