When preforming image co-registration of multiple subjects, how should we select the reference image?
Can a randomly selected image form one dataset could be the reference image for an image from the other dataset?
If we do that, should all the images belonging to the reference image dataset be co-registered with the reference image as well?
I couldn't find any material in this area. Could someone please advice?
I'm not sure exactly what you mean by the term "dataset", but I will assume you are asking about co-registering multiple images from different patients (i.e. multiple 3D images per subject).
To answer your questions:
If there are no obvious choices about which image is best, then a random choice is fine. If you have e.g. a CT and an MRI for each subject, then co-registration using the CT images is likely going to give you better results because of intrinsic image characteristics (e.g. less distortion, image value linked to physical quantity).
I suppose that depends on what you want to do, but if it is important to have all imaging data in the same co-registered reference space then yes.
Another option is to try and generate an average image, and then use that as a reference to register other images to. Without more information about what you are trying to achieve it's hard to give any more specific advice.
Related
image stitching with a reference image.
I have multiple images of subject(bone), the images are of different sections of the subject as on a 3x3 matrix. I would like to stitch them together but the problem is they don't have any common feature, as the subject was cut into these sections using a saw. What i have is the image of subject before cutting and want to use it as a guide to stitch the images of sections together.
I have tried using Fiji imagej and searched the web for an alternative. imageJ can only do the job if it has common feature between images to work with. can someone point to some code in python or matlab that can do this or any software that could help.
'[Reference image][1] section (11) section (12) section (13) section (21) section (23) section (31)'
' [1]: https://i.stack.imgur.com/wQr09.jpg
I'm not able to add more than 8 links due to SO's policy. There are two more remaining, I'll add them soon. And the "section (22)" i.e centre position in the 3X3 matrix is empty.
Solutions for image processing needs like this vary wildly depending on whether you need a script to use just a few times, a software tool you'll use for a few weeks, or what could become lab automation software.
This seems to be a problem more of image matching rather than image stitching. By image matching I mean you need to find out how a subimage such as the bone section at (row 2, column 1) would match what is labeled as "4," the center left section, in the reference bone image.
The basic process:
Load your reference image as a 2D array (first converted to grayscale)
Load your first sample image of a subsection of bone.
Use an algorithm such as SIFT to determine the location, orientation, and scale to fit the bone subsection image onto the reference image.
Apply the fit criteria (x,y,rotation,scale) to the bone subsection image, transform it, and past it into a black image the same size as the reference image.
Continue the process above to fit all subsections.
(Optional) With all bone subsections fitted in place, perform additional image processing operations to improve the fit, fill in gaps, etc.
From your sample images it appears that the reference and the bone section images area taken using different lighting, sometimes with the flat portion of the bone slightly tilted relative to the camera's optical axis, etc., all of which makes the image match more difficult.
SIFT is an algorithm that could help here. Note that "scale invariant" is part of the algorithm name.
https://en.wikipedia.org/wiki/Scale-invariant_feature_transform
Given all that, your reference image and bone subsection images appear to be taken under very different circumstances, and that makes solving the problem harder than it needs to be. You'll have an easier time overall if you can control the conditions under which images are captured.
Capture all images with the same camera, with the same lighting, at roughly the same distance
For lighting, use something like a high-frequency diffuse fluorescent
Use the same background for every image (e.g. matte black)
Making this image match a robust process means paying attention to the physical setup as well as creating your image processing algorithm.
If you need a good reference for traditional image processing techniques, find a copy of Digital Image Processing by Gonzalez and Woods. Some time spent with that book will give you better answers faster than learning image processing piecemeal online.
For practical image processing that addresses real-world concerns for implementing even simple image processing algorithms, look for Machine Vision by Davies.
I would strongly urge that you NOT look into machine learning, or try to find an answer in a more advanced image processing textbook until you run into a roadblock with more traditional methods.
I have to count the number of checked and unchecked boxes in a paper sheet.The size of the checkbox is very small.Which will be the best object detection algorithms for this or any other approach.I have some images on which I can do customized training.Note my task is only object detection & recognition not localization.One approach is to extract the portion of the image which is containing the check boxes & apply contours to classify which is checked or unchecked.My question how I will extract that portion of an image which is containing scanned document or sheet.
I think you have to use Convolutional Neural Network it is the best object detection algorithm that I have ever used, although the matter of small objects this algorithm is so good at identifying small hidden patterns so, I think its work best for you, just try it.
Thanks for dropping in here.
I'm currently working on a project, and I'm not that strong with python yet. So I was hoping for some constructive feedback on this question.
I have a dataset containing core samples, all stored with sample id, latitude, longitude, content and other data irrelevant for this question.
Now I've imported this dataset and sliced it as I want it to be. For the images I'm using the rasterio module to open 2 satellite images that covers the region. I'm using the utm module to convert back and forth between latlong->UTM->Pixel values (Which also seems to be throwing me strange coordinates at some points).
Annoyingly enough, the two Sentinel-2 images are cut right across the center of the map.
As I'm doing bounding boxes on top of where the samples are taken, this is a problem as I need to extract 10x10 pixel cut outs of that region. This leads to a lot of the samples not getting a proper cut out.
So I thought why not merge the two images together into one large rectangular bit. But I still need to retain the meta data with the UTM coordinates.
How would you suggest I proceed. Can it be done in an easy way? Is there another angle on this I've overlooked?
Thank you for your time.
I'm not sure I completely understand the question, but if you are simply trying to merge 2 images, have you looked at the command line tool gdal_merge.py?
A very simple example:
gdal_merge.py -o merged_image.tif image1.tif image2.tif
I'm sorry if this is a simple question...but may I ask how can I use online datasets to obtain segmented object images (and hence be able to generate masks from it)? I'll need both the segmented object and the masks afterwards.
For example, I need to use datasets that will produce results on the right:
Image taken from: https://www.researchgate.net/figure/Example-of-GrabCut-segmentation_fig2_282740894
I don't really want to segment images myself and would rather use categorised datasets instead.So basically, I'll want to be able to search for an object type (e.g. bikes) and have a large set of segmented bikes. (Basically, have large sets of segmented object images grouped and stored by categories.)
I know that COCO dataset has categorised and annotated objects. But I'm not sure how to use it...Or if there's better ways of achieving my goal, what would it be? Would someone please help? Thanks a lot!
I am doing some studies on eye vascularization - my project contains a machine which can detect the different blood vessels in the retinal membrane at the back of the eye. What I am looking for is a possibility to segment the picture and analyze each segmentation on it`s own. The Segmentation consist of six squares wich I want to analyze separately on the density of white pixels.
I would be very thankful for every kind of input, I am pretty new in the programming world an I actually just have a bare concept on how it should work.
Thanks and Cheerio
Sam
Concept DrawOCTA PICTURE
You could probably accomplish this by using numpy to load the image and split it into sections. You could then analyze the sections using scikit-image or opencv (though this could be difficult to get working. To view the image, you can either save it to a file using numpy, or use matplotlib to open it in a new window.
First of all, please note that in image processing "segmentation" describes the process of grouping neighbouring pixels by context.
https://en.wikipedia.org/wiki/Image_segmentation
What you want to do can be done in various ways.
The most common way is by using ROIs or AOIs (region/area of interest). That's basically some geometric shape like a rectangle, circle, polygon or similar defined in image coordinates.
The image processing is then restricted to only process pixels within that region. So you don't slice your image into pieces but you restrict your evaluation to specific areas.
Another way, like you suggested is to cut the image into pieces and process them one by one. Those sub-images are usually created using ROIs.
A third option which is rather limited but sufficient for simple tasks like yours is accessing pixels directly using coordinate offsets and several nested loops.
Just google "python image processing" in combination with "library" "roi" "cropping" "sliding window" "subimage" "tiles" "slicing" and you'll get tons of information...