case1.5dfn.slc: Segment Data using Anisotropic Diffusion Method on each Slice Separately

 

Input Data Options
	special purpose data requiring pre-processing (0)
	tomographic data (1)
	segmented data (2)
	burn data (3)
	medial axis data (4)
	throat data (5)
Enter choice: 1

Data Processing Options
	plot image (1)
	plot slice histograms (2)
	resize tomographic data (3)
	generate circular fiducial polygon (4)
	segment data (5)
	porosity/cut_off dependence (6)
	investigate dfn K values (7)
	investigate dfn threshold values (8)
	|grad image| histogram (9)
	compress data in z direction (10)
Enter choice: 5

Segment by
	simple threshold (smpl)
	anisotropi diffusion (dfn)
	indicator kriging (krig)
	Mardia-Hainsworth (mh)
	color values (hls)
Enter choice: dfn

The tomographic data file is 
	raw data file (raw)
	partially diffused data file (dfn)
Enter choice: raw

Tomographic data slices are contained in
	separate files (slc)
	a single file  (vol)
Enter file type (slc,vol): slc

Recognized tomographic data file types are
	 ascii integer         array  (  ai)
	binary integer         array  (  bi)
	swapped binary integer array  ( sbi)
	binary short integer   array  (  bs)
	swapped binary short   array  ( sbs)
	binary unsigned char   array  ( ubc)
	netcdf files                  (  nc)
	netcdf ascii files            (  na)
	Kimberly Clark data           (  kc)
	punch ascii files             (  pa)
	sun rasterfile ( 8-bit color) ( sr8)
	sun rasterfile (24-bit color) (sr24)
Enter data type (ai,bi,sbi,bs,sbs,ubc,nc,na,kc,pa,sr8,sr24): bi

    Files assumed to be labelled  basename.ext,
    basename  is limited to 255 characters maximum,
    ext       is a numerical designator lying between 000 and 999
    It is assumed that the files have consecutively numbered extensions
        ie.  000 -> 056,  or   021 -> 049

Enter basename for tomographic files: ../../Examples/data/berea/berea
Are files compressed? [y,n]: y

Enter first and last slice of data to use: 1 3
Slice size of data, (nx,ny): 541 541

Fiducial polygon generation
	NONE         (N)
	MANUALLY set (M)
Enter method: M

	MANUAL fiducial boundary calculation

	The bounding curve for each fiducial polygon should be convex in
	the sense that at most 2 points on the bounding curve have the
	same y coordinate.
Enter number of polygons: 1

Are the coordinates of the fiducial polygon(s) stored in a file: y
	Filename of fiducial polygon 1: ../../Examples/data/polygon.541

Select diffusion algorithm
	Diffuse entire volume (v)
	Diffuse   by   slice  (s)
Enter choice (v,s(dflt)): s
Enter range [min_val,max_val] to search for valley location: 10000 90000

The diffusion iteration schedule is indicated schematically below

	|   K1  |   K2  |  .....  |  Kn   |  Kn   |  Kn  |  ....   |

	0      I       I         I       I       +dI   +2dI  ...  I
	        1       2         n-1     min                      max

	Iterations  0 -> I1 are done at the diffusion value K1
	Iterations I1 -> I2 are done at the diffusion value K2

	Iterations In-1 -> Imin are done at the value Kn

	Iteration Imin -> Imax continue to be done at Kn. However each
	dI iterations the histogram is checked to determine in any bin
	has fallen below 'min_count' counts. The schedule is stopped
	if either 'Imax' iterations or 'min_count' counts are reached.

Enter 'n', number diffusion values, to be used: 1

Enter 1  K[i], I[i] pair values, 1 pair per line
	: 3 10

Enter maximum  number (max_nit) of diffusion iterations: 15
Enter interval number (del_nit) of diffusion iterations: 5
Enter the desired minimum number (min_count) of counts: 100

Enter basename for diffused files: dfn/berea
Plot l2-norm change in image under diffusion? (y,n(dflt)): y
Select method for determining MIN and MAX values for scaling data
	volume          (v)
	slice           (s)
	slice  adjusted (sa)
Enter choice: sa
Enter minimum counts to determine adjusted values at low and high end
of histogram. Separate values for the minimum counts may be specified
at each end.
	low  end value: 100
	high end value: 100

Segmentation methods require binning data
Default histogram contains 128 bins. Change number of bins? (y,n(dflt)): n

Prepare histogram files of diffused image (y,n(dflt)): y

Merge histogram files of diffused image (y,n(dflt)): y
Number of histogram files to be merged per page: 3
histogram files arranged nrows by ncols on a page
Enter nrows and ncols: 2 2

Enter the title for histogram: berea

Enter the data label for histogram: \0

Prepare raster files of diffused image (y,n(dflt)): y
Rasterfile colormap choices
	rainbow			      (r)
	greyscale with void     white (gv)
	greyscale with material white (gm)
	pinkscale with void     pale  (pv)
	pinkscale with material pale  (pm)
Enter choice (r,gv(dflt),gm,pv,pm): gv

Merge raster files of diffused image (y,n(dflt)): y
Number of diffused files to be merged per page: 3
Are files compressed? [y,n]: y
Enter basename for merged_diffused files: dfn/berea_mrgd
Specify layout of 3 merged diffused files
	Regular matrix arrangment (r)
	user specified arrangment (u)
Enter choice (r(dflt),u): r
diffused files arranged nrows by ncols on a page
Enter nrows and ncols: 2 2

Delete single slice rasterfiles after merging? (y,n(dflt)): y

Prepare bit files of segmented image (y,n(dflt)): y
Enter basename for segmented files: seg/berea

Prepare raster files of segmented image (y,n(dflt)): y

Merge raster files of segmented image (y,n(dflt)): y
Number of segmented files to be merged per page: 3
Are files compressed? [y,n]: y
Enter basename for merged_segmented files: seg/berea_mrgd
Specify layout of 3 merged segmented files
	Regular matrix arrangment (r)
	user specified arrangment (u)
Enter choice (r(dflt),u): r
segmented files arranged nrows by ncols on a page
Enter nrows and ncols: 2 2

Delete single slice rasterfiles after merging? (y,n(dflt)): y