mtteach(1) General Commands Manual mtteach(1)
NAME
mtteach - develop criteria for feature recognition
SYNOPSIS
mtteach
DESCRIPTION
This program is used to develop criteria for distinguishing micro-
tubules (or other small features) from other image features. First one
prepares a file of teaching points. This file is built on a series of
areas and contains a point for each real feature in each area. The
first two points in each area are two diagonal corner points of the
area, NOT features; the rest of the points for that area should all be
inside these points. The program has modest ability to check for
errors in the teaching file. It is permissible to add a point to the
teaching point list that falls in ANY of the zones defined earlier in
the list, not necessarily in the last defined zone.
Given the zones described by the areas with teaching points, the pro-
gram reads in the images contained in these zones (these images would
typically already be filtered, or correlated with the average feature).
The program then allows one to set up a two-step procedure for identi-
fying features. In the first step, the program finds all of the points
where the pixel value is more extreme than a threshold value. One can
adjust this threshold so as to find as many of the true features as is
feasible, at the expense of finding many false features as well. In
the second step, the program uses discriminant analysis on the pixels
surrounding the extreme peaks to find a discriminant function (a func-
tion of those surropunding pixels) that best distinguishes the peaks
that are true features from peaks that are not.
It is possible either to examine single pixels to look for peaks, or to
form a simple sum of pixels within a circular window around each pixel
position, and look for peaks in those "window sums". In practice this
seems to be useless, so the instructions below will refer to examining
pixels rather than "window sums".
When a prompt prints a number in [ ], that is a default value which can
be selected by entering "/"
ENTRIES to the program:
Image file name Name of file of piece coordinates if image is a mon-
tage, otherwise Name of teaching point file
0 use individual pixel values to look for peaks,
or 1 to use sums of pixels within a circular window.
IF you select sums of pixels, then enter two lines:
Radius of the window in pixels (which need not be an integer)
0 to center window on a pixel, 1 to offset it by (0.5,0.5)
Distance to search from a peak to eliminate nearby extreme points.
When the program finds a pixel more extreme than the threshold
(specified below), it will search up to this distance away for
other points above threshold, and record only a single peak at the
most extreme pixel.
Number of pixels at edges of zones to leave out of search for peaks.
Typically one would enter the feature radius here.
At this point, one may repeatedly enter threshold values. The threshold
can be either an absolute value (if>1) or a relative value (between 0
and 1). The program will search for pixels greater or less than the
threshold depending on whether an absolute threshold is greater or less
than the mean, or a relative one is greater or less than 0.5. If one
enters a relative threshold, then within each separate zone, the pro-
gram will look for a fixed fraction of extreme pixels; i.e. a threshold
of 0.99 or 0.01 will select the brightest or dimmest 1% of pixels for
further consideration. For each threshold value, the program outputs
the number of teaching points that have been found and the number of
extreme points (after contiguity elimination) that do not correspond to
teaching points.
Instead of selecting a threshold, one may enter:
-3 to output points above threshold to a file - useful only for
diagnostic purposes (one needs to specify a threshold again)
-2 to loop back and respecify whether to do window sums, the
search radius for eliminating contiguous peaks, and the edges
-1 to do discriminant analysis, if there are fewer than 1000 peaks
When discriminant analysis is selected, if there are any true features
that are not peaks above threshold, then the program will ask whether
to include those points as true features in the analysis. Enter 0 to
exclude them or 1 to include them.
Next specify a division of a circular region into a series of sectored
rings, by entering:
Radius of the entire circular region Radius of central circle, which is
not divided into sectors Annular thickness (outer minus inner radius)
of the innermost
ring, the one outside the central, undivided circle Number of con-
centric rings (excluding central circle) Number of sectors to divide
rings into 0 to center the rings on a pixel, 1 to offset by (0.5,0.5)
The thickness of the rings will change (typically increase) progres-
sively from the specified thickness of the innermost ring so as to fit
the given number of rings into the desired total circular area.
The program makes a variable out of the sum of pixels in each sector,
then finds a discriminat function, a linear combination of these vari-
ables, that best distinguishes the true peaks from the false ones. It
then finds a criterion discriminant score that minimizes the total num-
ber of extreme points that would be misclassified.
The choices after a discriminant analysis are:
1 to scan a range of discriminant scores, finding the criterion in
that range that minimizes misclassified peaks. Enter a lower and
upper criterion score and the number of scores to try in the range
2 to redo discriminant analysis after respecifying sectored rings
3 to store analysis parameters and solutions in a file for use by
Mtdetect (the last "best criterion score" found by scanning is the
value that will be stored in the file)
4 to loop back to setting thresholds for peak selection
5 to store the discriminant function in an image file as a
coefficient for each pixel
6 to exit
HISTORY
Written by David Mastronarde, May 1989
BUGS
Email bug reports to mast at colorado dot edu.
IMOD 5.2.0 mtteach(1)