Hosuk:LabNotes/2014-8-22: Difference between revisions
Jump to navigation
Jump to search
>Hosuki78 (Created page with "*LabNote ===Decoding with Agi26k0gap Padlock Probe : Masking images with MIP of all steps=== ====Masking==== *Done by Matt *MIP with images of all steps ...") |
>Hosuki78 No edit summary |
||
Line 7: | Line 7: | ||
*MIP with images of all steps | *MIP with images of all steps | ||
*Run PISA of the MIP | *Run PISA of the MIP | ||
seg_data = regionprops(MaskedFile,MaskedImgFile, 'MeanIntensity', 'Area', 'Centroid'); | **seg_data = regionprops(MaskedFile,MaskedImgFile, 'MeanIntensity', 'Area', 'Centroid'); | ||
Line 21: | Line 21: | ||
**The server hosting the source code no longer exists. The "newer" version of the software was written in C (http://arep.med.harvard.edu/Polonator/2006/image_processing_pipeline/). Not sure whether that's something you can pick up immediately. | **The server hosting the source code no longer exists. The "newer" version of the software was written in C (http://arep.med.harvard.edu/Polonator/2006/image_processing_pipeline/). Not sure whether that's something you can pick up immediately. | ||
====Variables==== | |||
*After masking | |||
**DecodeInfoMaxNum : Total number of features by regionprops | |||
**DecodeInfo_MeanIntensity(1:MaxRolonyNum, 1:NumOfSteps) : Mean intensity of each feature | |||
**RolonyInfo_Area(1:MaxRolonyNum, 1): Area(number of pixels) of each feature | |||
**RolonyInfo_CenterY(1:MaxRolonyNum, 1) : Centroid X of each feature | |||
**RolonyInfo_CenterX(1:MaxRolonyNum, 1): Centroid Y of each feature | |||
*After normalize | |||
**Max_MeanIntensity(1:MaxRolonyNum, 1) : Max intensity of each Step1 ~ Step21 | |||
**DecodeInfo_NormMeanIntensity(1:MaxRolonyNum, 1:NumOfSteps) : Normalized by Max | |||
***DecodeInfo_NormMeanIntensity = DecodeInfo_MeanIntensity / Max_MeanIntensity; | |||
**DecodeInfo_Sqrt(1:DecodeInfoMaxNum, 1:NumOfDecodingSlots) : sqrt of sum of sq of each intensity = sqrt[ (I_488)^2 + (I_Cy3)^2 + (I_Cy5)^2 ] | |||
**DecodeInfo_NormMeanIntensity2(1:MaxRolonyNum, 1:NumOfSteps) : norm intensity after filtered out the weak signal | |||
*** all intensity = 0 when DecodeInfo_Sqrt < thresh_val (it is 0.1 for now) | |||
**DecodeInfo_NormMeanIntensity_Result(1:MaxRolonyNum, 1:NumOfSteps) : normalized again by norm_tetra_3, plot cloud with this | |||
***[DecodeInfo_NormMeanIntensity_Result, cms, calls, coords_3d] = norm_tetra_3(DecodeInfo_NormMeanIntensity2, NumOfSteps, thresh_subvalue); | |||
====Normalized intensity distribution==== | |||
*Each feature has Mean | |||
*Image resolution : 20x obj, 4096 x 4086, z step = 1.04um | *Image resolution : 20x obj, 4096 x 4086, z step = 1.04um | ||
**Since how many features would be detected or how much dense rolonies in this sample, so image in wide field of view | **Since how many features would be detected or how much dense rolonies in this sample, so image in wide field of view | ||
**eventually 63x obj. | **eventually 63x obj. | ||
====Result==== | |||
[[File:DecodeResultSummary_1_2014-08-21.png|600px]] |
Revision as of 22:49, 22 August 2014
Decoding with Agi26k0gap Padlock Probe : Masking images with MIP of all steps
Masking
- Done by Matt
- MIP with images of all steps
- Run PISA of the MIP
- seg_data = regionprops(MaskedFile,MaskedImgFile, 'MeanIntensity', 'Area', 'Centroid');
- Matlab scripts
- PISA_MaskImages_4d.m
- masking, and get mean intensity, area, (x, y) position
- normalize mean intensity of each dye using norm_tetra_3.m
- Plotting_3.m : Plot cloud plot
- norm_tetra_3.m : normalize intensity (sqrt...),
- tetrahedron_4.m : Plot 3(or 4) colors on tetrahedron space
- http://arep.med.harvard.edu/Polonator/2006/image_processing_pipeline/DATASETS/
- http://www.sciencemag.org/content/suppl/2005/10/12/1117389.DC1/Shendure.SOM.pdf
- The server hosting the source code no longer exists. The "newer" version of the software was written in C (http://arep.med.harvard.edu/Polonator/2006/image_processing_pipeline/). Not sure whether that's something you can pick up immediately.
Variables
- After masking
- DecodeInfoMaxNum : Total number of features by regionprops
- DecodeInfo_MeanIntensity(1:MaxRolonyNum, 1:NumOfSteps) : Mean intensity of each feature
- RolonyInfo_Area(1:MaxRolonyNum, 1): Area(number of pixels) of each feature
- RolonyInfo_CenterY(1:MaxRolonyNum, 1) : Centroid X of each feature
- RolonyInfo_CenterX(1:MaxRolonyNum, 1): Centroid Y of each feature
- After normalize
- Max_MeanIntensity(1:MaxRolonyNum, 1) : Max intensity of each Step1 ~ Step21
- DecodeInfo_NormMeanIntensity(1:MaxRolonyNum, 1:NumOfSteps) : Normalized by Max
- DecodeInfo_NormMeanIntensity = DecodeInfo_MeanIntensity / Max_MeanIntensity;
- DecodeInfo_Sqrt(1:DecodeInfoMaxNum, 1:NumOfDecodingSlots) : sqrt of sum of sq of each intensity = sqrt[ (I_488)^2 + (I_Cy3)^2 + (I_Cy5)^2 ]
- DecodeInfo_NormMeanIntensity2(1:MaxRolonyNum, 1:NumOfSteps) : norm intensity after filtered out the weak signal
- all intensity = 0 when DecodeInfo_Sqrt < thresh_val (it is 0.1 for now)
- DecodeInfo_NormMeanIntensity_Result(1:MaxRolonyNum, 1:NumOfSteps) : normalized again by norm_tetra_3, plot cloud with this
- [DecodeInfo_NormMeanIntensity_Result, cms, calls, coords_3d] = norm_tetra_3(DecodeInfo_NormMeanIntensity2, NumOfSteps, thresh_subvalue);
Normalized intensity distribution
- Each feature has Mean
- Image resolution : 20x obj, 4096 x 4086, z step = 1.04um
- Since how many features would be detected or how much dense rolonies in this sample, so image in wide field of view
- eventually 63x obj.