- One image at a time. If your computer memory permits, load them all to a single 3D variable.
- You can either determine a threshold manually, or you need to implement some method to automatically determine the threshold. Depending on your data you may need to do some filtering if a threshold depends on regional values (eg, in this array [1 2 3 1 3 4 5 6 7 5 7 8], both 1 and 5 could be considered low values, because of the surrounding values).
- Once you have a logical array that is true for every voxel with the defect and false otherwise, sum(L(:)) will count the number of voxels. You can do a similar thing to create a vector of defect-voxels per slice and use the second output of max.
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I wish to process around 2000 CT images. these are CT image of Material. I wish to find the size of 3D inclusion.
- Read around 2000 CT images of a metal specimen.
- defect in metal are dark in color. I wish to calculate the 3d size of the defect.
- How to read 2000 CT images ..
- how to set the threshold intensity for identification of defect in the CT image
- how to calculate the 3D size of the defect and the CT image in which the 2d area of the inclusion is maximum.
25 comentarios
- Should i do the contrast adjustmend of each image before importing them to a 3d Array. or after the formation of 3D array.
- The regionprops gives the dimension like volume and other details of the defects. but is there anyother way to identify them so that it is easier to identify the value corresponds to whivch defect.
- How can i find the Z-projection area of the defect. U explained in previous comment but i could not understand.
- I need to ignore the defects below a certain size-projection area how can i modify the program to see only the defects above that size.
- Doesn't matter. As long as you're consistent.
- The centroid property gives you the centroid of the region (so it contains an approximation of the center point). That should work well enough for the location.
- If you use a labeled image as input (by using bwlabel on binarizeVolume), you can use the centroid to determine the label of a particular defect. Then you can use that on the result of bwlabel to make a logical array with only that defect. Using sum or max will give you a projection.
- Loop through all defects to check the volume. If it is too small, mark it with false on the binarized image.
- For preprocessing of Image : I have following questions:
- If you use a labeled image as input (by using bwlabel on binarizeVolume), you can use the centroid to determine the label of a particular defect. Then you can use that on the result of bwlabel to make a logical array with only that defect. Using sum or max will give you a projection.
- Loop through all defects to check the volume. If it is too small, mark it with false on the binarized image."
- i agree with you there is some problem in binarization, Can you tell me how do i make pixel intensity below 40 as white and above 40 as black. what synatx should i use.
- 2. the other problem which i am facing is that at the circumference of the material there is dark line, which is also turned black during binarization.
- how do i remove this dark line at the circumference ?
- How do i define connectivity of pixel in region props ?
- Can u guide How can i write more data to stats table generated by regionprops3.
- can i specify limits in regionprops3 to get the properties of object above a certain limit(such as volume above 500 or equivalentdiameter above 5 ?
- Can i specify limits in bwlabeln to label only objects which are above certain size ?
- is there a method of labeling before regionprops without running out of memory.
- Can you please guide me how can i modify stats table(obtaines by regionprops3) based on the table properties. (i wish to delete certain rows based on the one or two attributes of the table).
- Delete the rows in the table (without modifying the label array)
- Modify the label array
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