Dicom - normalization and standardization

Question

I am new to the field of medical imaging - and trying to solve this (potentially basic problem). For a machine learning purpose, I am trying to standardize and normalize a library of DICOM images, to ensure that all images have the same rotation and are at the same scale (e.g. in mm). I have been playing around with the Mango viewer, and understand that one can create transformation matrices that might be helpful in this regard. I have however the following basic questions:

• I would have thought that a scaling of the image would have changed the pixel spacing in the image header. Does this tag not provide the distance between pixels, and should this not change as a result of scaling?
• What is the easiest way to standardize a library of images (ideally in python)? Is it possible and should one extract a mean pixel spacing across all images, and then scaling all images to match that mean? or is there a smarter way to ensure consistency in scaling and rotation?

Many thanks in advance, W

Answer 1

Does this tag not provide the distance between pixels, and should this not change as a result of scaling?

Think of the image voxels as fixed units of space, which are sampling your image. When you apply your transform, you are translating/rotating/scaling your image around within these fixed units of space. That is, the size and shape of the voxels doesn't change. They just sample different parts of your image.

You can resample your image by making your voxels bigger or smaller or changing their shape (pixel spacing), but this can be independent of the transform you are applying to the image.

What is the easiest way to standardize a library of images (ideally in python)?

One option is FSL-FLIRT, although it only accepts data in NIFTI format, so you'd have to convert your DICOMs to NIFTI. There is also this Python interface to FSL.

Is it possible and should one extract a mean pixel spacing across all images, and then scaling all images to match that mean? or is there a smarter way to ensure consistency in scaling and rotation?

I think you'd just to have pick a reference image to register all your other images too. There's no right answer: picking the highest resolution image/voxel dimensions or an average or some resampling into some other set of dimensions all sound reasonable.