http://stage.nitrcce.org/forum/forum.php?forum_id=5754 General Discussion en-us Copyright 2000-2026 NITRC OSI Fri, 10 Apr 2026 11:16:02 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator http://stage.nitrcce.org/forum/forum.php?thread_id=15003&forum_id=5754 &lt;p&gt;Hi,&amp;nbsp;&lt;/p&gt;<br /> &lt;p&gt;I am using DRAMMS for registration and I'm interested in evaluating the accuracy based on the deformation fields. ie. if I add the deformation field value at each pixel location, I should reach the corresponding pixel in the output image, which should be the same as the fixed image. However, the deformation field saved by DRAMMS has a very unusual format, it seems to be very sparse and pixelated. I've attached an image. This is an issue because when I deform my image points using the field, I do not reach the same warped image as DRAMMS.&amp;nbsp;&amp;nbsp;&lt;/p&gt;<br /> &lt;p&gt;I am wondering if there is a way to format this to be more continuous. I saw there is a function called ConvertDeformation, but my installation didn't seem to produce an executable for it and I can't seem to be able to manually produce it. Any help would be greatly appreaciated.&lt;/p&gt;<br /> &lt;p&gt;Kind regards,&lt;/p&gt;<br /> &lt;p&gt;Clara&amp;nbsp;&lt;/p&gt;<br /> &lt;p&gt;&amp;nbsp;&lt;/p&gt;<br /> &lt;p&gt;&lt;img 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&quot; width=&quot;330&quot; height=&quot;318&quot;&gt;&lt;/p&gt; Clara Rodrigo Gonzalez Tue, 12 Mar 2024 15:46:36 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=15003&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 Hi,<br /> <br /> I would like to try this toolbox, but I have the same issues. I try to read in nii.gz files but it shows an error. Did anyone find out how to fix this?<br /> <br /> best,<br /> C cst_333_333 Thu, 17 Mar 2022 12:47:00 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=12833&forum_id=5754 Hi there,<br /> <br /> I was wondering if it is possible to get the list of the coordinates of the control points used at the last iteration (final resolution).<br /> I tried to use the option "-i" when calling DRAMMS from terminal, though I could find that kind of information.<br /> <br /> I call DRAMMS with options "-x 9 -y 9 -z 1", so I am expecting a grid with spacing of 9 voxels in the XY plane and 1 voxel along the z-direction.<br /> <br /> I think control points are evaluated during the execution of the function .../dramms-1.5.1/lib/Deform3D. However, I believe it is a x-sharedlib (and I am not sure how to read / modify code lines).<br /> <br /> I was wondering if you could help me with this.<br /> <br /> Many thanks,<br /> Stefano. Stefano Zappalà Wed, 13 Oct 2021 9:23:22 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=12833&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 Hi there! <br /> <br /> Did you end getting to solve this issue?<br /> <br /> I have just downloaded DRAMMS and faced the same...<br /> <br /> Cheers<br /> <br /> Luís Lu La Mon, 29 Mar 2021 14:04:24 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=11125&forum_id=5754 Hello, <br /> <br /> I need help please. I tried to register 2 sacral CT but the procedure fails...<br /> Can you help me please ? <br /> <br /> Here is what the verbose showed : <br /> <br /> (base) [planet@lito-orsay Desktop]$ dramms -S /home/planet/Desktop/MAR_CT_RECID.nii.gz -T /home/planet/Desktop/MAR_CT_PRERT.nii.gz -O /home/planet/Desktop/MAR_CT_RECID2PRERT.nii.gz -D /home/planet/Desktop/MAR_CT_RECID2PRERT_DEF.nii.gz -v<br /> Automatically mapping regularization weights and label factor to roughly the range [0,1]<br /> Regularization = .35555555555555555554<br /> Label factor = 0.50<br /> <br /> Number of Gabor scales = 3<br /> -------------------------------------------------------------------------------------------<br /> DRAMMS: Deformable image Registration via Attribute Matching and Mutual-Saliency weighting<br /> -------------------------------------------------------------------------------------------<br /> ----------------------------------------<br /> Registering from<br /> MAR_CT_RECID.nii.gz (in directory /home/planet/Desktop)<br /> to<br /> MAR_CT_PRERT.nii.gz (in directory /home/planet/Desktop),<br /> Registered image will be<br /> MAR_CT_RECID2PRERT.nii.gz (in directory /home/planet/Desktop)<br /> and deformation will be<br /> MAR_CT_RECID2PRERT_DEF.nii.gz (in directory /home/planet/Desktop).<br /> Both registered image and deformation field will be in the same space as MAR_CT_PRERT.nii.gz.<br /> ----------------------------------------<br /> Gabor attributes will be computed at 3 scales and in 4 orientations<br /> During discrete optimization, number of samples: 5<br /> Manipulation method of control points: 1<br /> Regularization weight: 0.2<br /> Input mask: NULL<br /> Temporary working directory: /tmp/dramms-VqBDLo<br /> Temporary attribute directory: /tmp/dramms-VqBDLo/features<br /> Intermediate results directory: /tmp/dramms-VqBDLo/intermediate<br /> Changed to directory /tmp/dramms-VqBDLo/intermediate<br /> Step 1: Convert images to byte datatype...<br /> ++ WARNING: nifti_read_buffer(/home/planet/Desktop/MAR_CT_PRERT.nii.gz):<br /> data bytes needed = 2048<br /> data bytes input = 49<br /> number missing = 1999 (set to 0)<br /> ++ WARNING: nifti_read_buffer(/home/planet/Desktop/MAR_CT_PRERT.nii.gz):<br /> data bytes needed = 2048<br /> data bytes input = 49<br /> number missing = 1999 (set to 0)<br /> Step 2: Match histograms if necessary...<br /> Start to estimate histogram...<br /> TotalIntensityA=1568979839.000000 NonZeroPntsA=18518625.000000<br /> TotalIntensityB=165014657.000000 NonZeroPntsB=9649350.000000<br /> cumulative histogram for A = 1.000000, for B = 1.000000<br /> Entropy A = 4.240346<br /> Entropy B = 2.334679<br /> cumulative histogram for image A:<br /> 0.316329 at intensity 12 (perhaps noise),<br /> 0.417674 at intensity 51 (lower 1/5 intensity range),<br /> 0.984657 at intensity 204 (higher 1/5 intensity range).<br /> cumulative histogram for image B:<br /> 0.103504 at intensity 12 (perhaps noise),<br /> 0.997653 at intensity 51 (lower 1/5 intensity range),<br /> 1.000000 at intensity 204 (higher 1/5 intensity range).<br /> No need to match histogram.<br /> Compare image similarity before and after histogram matching.<br /> before: cc*mi = .715031506181<br /> after : cc*mi = .715031506181<br /> We decided to keep byte images after histogram matching, as histomatch increased image similarity.<br /> Step 3: Affine registration of images by FSL's flirt tool (may take several minutes)...<br /> Try using normmi as similarity metric for affine registration, search range [-180, 180]<br /> Try using corratio as similarity metric for affine registration, search range [-180, 180]<br /> Selecting best affine registration results<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.032195984: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.0217732997: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.01392433965: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.0007671974302: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.00972434: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: -0.002290940197: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: 0.01662321697: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: 0.007026530255: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.346018679: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi180: mi_inter=1.561389, cc_inter=0.964641, mi_inter*cc_inter=; mi_full=1.356784, cc_full=0.544964, mi_full*cc_full=.739398435776<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.033756639: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.02667788233: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.002680037682: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.02833808746: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.032154778: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: 0.0214324315: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: -0.009079334872: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: -0.07486613408: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.4767429578: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio180: mi_inter=1.539236, cc_inter=0.965485, mi_inter*cc_inter=; mi_full=1.346118, cc_full=0.654469, mi_full*cc_full=.880992501342<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> Try using normmi as similarity metric for affine registration, search range [-45, 45]<br /> Try using corratio as similarity metric for affine registration, search range [-45, 45]<br /> Selecting best affine registration results<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.032195984: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.0217732997: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.01392433965: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.0007671974302: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.00972434: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: -0.002290940197: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: 0.01662321697: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: 0.007026530255: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.346018679: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi180: mi_inter=1.561389, cc_inter=0.964641, mi_inter*cc_inter=; mi_full=1.356784, cc_full=0.544964, mi_full*cc_full=.739398435776<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.033756639: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.02667788233: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.002680037682: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.02833808746: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.032154778: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: 0.0214324315: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: -0.009079334872: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: -0.07486613408: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.4767429578: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio180: mi_inter=1.539236, cc_inter=0.965485, mi_inter*cc_inter=; mi_full=1.346118, cc_full=0.654469, mi_full*cc_full=.880992501342<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.032127265: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.02199159156: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.01296402945: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.0007672555289: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.009802937: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: -0.002030155028: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: 0.009609101452: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: 0.00511685665: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.3920999932: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi45: mi_inter=1.553466, cc_inter=0.964789, mi_inter*cc_inter=; mi_full=1.352641, cc_full=0.582675, mi_full*cc_full=.788150094675<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.033504292: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.02611108137: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.002789337568: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.02830705073: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.033626168: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: 0.01791260079: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: -0.001202060891: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: -0.07452644167: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.4839075406: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio45: mi_inter=1.537860, cc_inter=0.965588, mi_inter*cc_inter=; mi_full=1.345989, cc_full=0.660152, mi_full*cc_full=.888557330328<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> Try using normmi as similarity metric, -nosearch -2D, for affine registration, default search range [-90, 90]<br /> Try using corratio as similarity metric, -nosearch -2D, for affine registration, default search range [-90, 90]<br /> Selecting best affine registration results<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.032195984: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.0217732997: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.01392433965: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.0007671974302: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.00972434: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: -0.002290940197: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: 0.01662321697: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: 0.007026530255: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.346018679: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi180: mi_inter=1.561389, cc_inter=0.964641, mi_inter*cc_inter=; mi_full=1.356784, cc_full=0.544964, mi_full*cc_full=.739398435776<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.033756639: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.02667788233: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.002680037682: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.02833808746: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.032154778: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: 0.0214324315: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: -0.009079334872: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: -0.07486613408: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.4767429578: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio180: mi_inter=1.539236, cc_inter=0.965485, mi_inter*cc_inter=; mi_full=1.346118, cc_full=0.654469, mi_full*cc_full=.880992501342<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.032127265: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.02199159156: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.01296402945: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.0007672555289: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.009802937: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: -0.002030155028: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: 0.009609101452: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: 0.00511685665: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.3920999932: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi45: mi_inter=1.553466, cc_inter=0.964789, mi_inter*cc_inter=; mi_full=1.352641, cc_full=0.582675, mi_full*cc_full=.788150094675<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 1.033504292: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.02611108137: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 337 : printf: -0.002789337568: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.02830705073: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 1.033626168: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 340 : printf: 0.01791260079: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 341 : printf: -0.001202060891: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 342 : printf: -0.07452644167: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.4839075406: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio45: mi_inter=1.537860, cc_inter=0.965588, mi_inter*cc_inter=; mi_full=1.345989, cc_full=0.660152, mi_full*cc_full=.888557330328<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 0.9999985099: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: 0.00171114353: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: -0.00171114353: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 0.9999985099: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 0.999999932: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=normmi_2D_nosearch: mi_inter=1.500247, cc_inter=0.963382, mi_inter*cc_inter=; mi_full=1.304275, cc_full=0.778788, mi_full*cc_full=1.015753718700<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> /home/planet/dramms/bin/dramms: ligne 335 : printf: 0.9999001026: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 336 : printf: -0.01413373964: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 338 : printf: 0.01413374301: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 339 : printf: 0.9998998642: nombre non valable<br /> /home/planet/dramms/bin/dramms: ligne 343 : printf: 1.000000033: nombre non valable<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 356 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 357 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 358 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 359 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 360 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 361 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 1: syntax error<br /> (standard_in) 2: syntax error<br /> when sim=corratio_2D_nosearch: mi_inter=1.500275, cc_inter=0.964146, mi_inter*cc_inter=; mi_full=1.306141, cc_full=0.782723, mi_full*cc_full=1.022346601943<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1439 : [: == : op????rateur unaire attendu<br /> (standard_in) 1: syntax error<br /> /home/planet/dramms/bin/dramms: ligne 1442 : [: == : op????rateur unaire attendu<br /> Error: Affine registration failed! No affine matrix was saved. thbscbr Sat, 28 Mar 2020 11:28:57 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=11125&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=10443&forum_id=5754 Dear experts,<br /> I want to install DRAMMS on my system which is linux, mint19. I use all the instructions in the site &quot;https://www.med.upenn.edu/sbia/dramms.html&quot; step by step. and I use ccmake as well. The cmake screen is attached to this e-mail so u can check it.<br /> First of all, when I push c button and then g, nothing gets created in the folder I define as cmake-install-prefix. and when I want to run DRAMMS I get the error &quot;command not found&quot; that shows it hasn't been installed successfully.<br /> I really need DRAMMS as soon as possible. I would be grateful if you could check my cmake screen and let me know where I am doing something wrongly.<br /> Best regards,<br /> Maedeh, Maedeh Khalilian Fri, 02 Aug 2019 14:11:19 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=10443&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 When I used an old version, it worked fine. But lately I downlosded new versions in ios, dramms does not seem to recognize inputs correctly. <br /> <br /> I tried pretty much all available versions, and they all have same issues. Is the code written errorously for mac (it does not make sense for me...) or is there any configuration that I missed here?<br /> <br /> Here's an example snapshot. The error occured that somehow dramms code errorneously included '/' in front of the input file name.<br /> <br /> <br /> <br /> <br /> seonjoosMacBook:127_S_0844-S22283-T1 seonjoolee$ /opt/sbia/dramms-1.4.3/bin/dramms -v -S 127_S_0844-S22283-T1_brain001.img -T MNI152_T1_1mm_brain001.hdr -O img_sub_2_mni.nii -D def_sub_2_mni.nii<br /> dyld: Library not loaded: /usr/local/opt/readline/lib/libreadline.7.dylib<br /> Referenced from: /usr/local/bin/awk<br /> Reason: image not found<br /> usage: dirname path<br /> usage: dirname path<br /> Automatically mapping regularization weights and label factor to roughly the range [0,1]<br /> Regularization = .35555555555555555554<br /> Label factor = 0.50<br /> <br /> Number of Gabor scales = 3<br /> -------------------------------------------------------------------------------------------<br /> DRAMMS: Deformable image Registration via Attribute Matching and Mutual-Saliency weighting<br /> -------------------------------------------------------------------------------------------<br /> ----------------------------------------<br /> Registering from<br /> 127_S_0844-S22283-T1_brain001.img (in directory )<br /> to<br /> MNI152_T1_1mm_brain001.hdr (in directory ),<br /> Registered image will be<br /> img_sub_2_mni.nii (in directory /Users/seonjoolee/Dropbox/127_S_0844-S22283-T1)<br /> and deformation will be<br /> def_sub_2_mni.nii (in directory /Users/seonjoolee/Dropbox/127_S_0844-S22283-T1).<br /> Both registered image and deformation field will be in the same space as MNI152_T1_1mm_brain001.hdr.<br /> ----------------------------------------<br /> Gabor attributes will be computed at 3 scales and in 4 orientations<br /> During discrete optimization, number of samples: 5<br /> Manipulation method of control points: 1<br /> Regularization weight: 0.2<br /> Input mask: NULL<br /> Temporary working directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-RtNDnS<br /> Temporary attribute directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-RtNDnS/features<br /> Intermediate results directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-RtNDnS/intermediate<br /> Changed to directory /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-RtNDnS/intermediate<br /> Step 1: Convert images to byte datatype...<br /> Failed to read image from file /127_S_0844-S22283-T1_brain001.img!<br /> Command /opt/sbia/dramms-1.4.3/lib/ConvertImage -s -e --reset-scaling -t uchar -m 0 -M 255 /127_S_0844-S22283-T1_brain001.img A_byte.nii.gz failed<br /> seonjoosMacBook:127_S_0844-S22283-T1 seonjoolee$ /opt/sbia/dramms-1.4.3/bin/dramms -v -S 127_S_0844-S22283-T1_brain001.hdr -T MNI152_T1_1mm_brain001.hdr -O img_sub_2_mni.nii -D def_sub_2_mni.nii<br /> dyld: Library not loaded: /usr/local/opt/readline/lib/libreadline.7.dylib<br /> Referenced from: /usr/local/bin/awk<br /> Reason: image not found<br /> usage: dirname path<br /> usage: dirname path<br /> Automatically mapping regularization weights and label factor to roughly the range [0,1]<br /> Regularization = .35555555555555555554<br /> Label factor = 0.50<br /> Number of Gabor scales = 3<br /> -------------------------------------------------------------------------------------------<br /> DRAMMS: Deformable image Registration via Attribute Matching and Mutual-Saliency weighting<br /> -------------------------------------------------------------------------------------------<br /> ----------------------------------------<br /> Registering from<br /> 127_S_0844-S22283-T1_brain001.hdr (in directory )<br /> to<br /> MNI152_T1_1mm_brain001.hdr (in directory ),<br /> Registered image will be<br /> img_sub_2_mni.nii (in directory /Users/seonjoolee/Dropbox/127_S_0844-S22283-T1)<br /> and deformation will be<br /> def_sub_2_mni.nii (in directory /Users/seonjoolee/Dropbox/127_S_0844-S22283-T1).<br /> Both registered image and deformation field will be in the same space as MNI152_T1_1mm_brain001.hdr.<br /> ----------------------------------------<br /> Gabor attributes will be computed at 3 scales and in 4 orientations<br /> During discrete optimization, number of samples: 5<br /> Manipulation method of control points: 1<br /> Regularization weight: 0.2<br /> Input mask: NULL<br /> Temporary working directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-ynxIZ8<br /> Temporary attribute directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-ynxIZ8/features<br /> Intermediate results directory: /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-ynxIZ8/intermediate<br /> Changed to directory /var/folders/57/31_68nmx40z1h0nkbp6l05bh0000gn/T/dramms-ynxIZ8/intermediate<br /> Step 1: Convert images to byte datatype...<br /> Failed to read image from file /127_S_0844-S22283-T1_brain001.hdr! Seonjoo Lee Tue, 16 Jul 2019 3:12:41 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=10380&forum_id=5754 http://stage.nitrcce.org/forum/forum.php?thread_id=6126&forum_id=5754 Welcome to Open-Discussion Yangming Ou Mon, 16 Nov 2015 2:16:36 GMT http://stage.nitrcce.org/forum/forum.php?thread_id=6126&forum_id=5754