I read dicom images with ITK using itk::ImageSeriesReader and itk::GDCMImageIO after reading i flip the images with itk::FlipImageFilter (to get right orientation of the images) and convert the itkImageData to vtkImageData using itk::ImageToVTKImageFilter. I visualization images with VTK using vtkResliceImageViewer in QVTKWidget2.
I set:
(vtkResliceImageViewer)m_imageViewer[i]->SetColorWindow(windowWidthTAGvalue[0028|1051]);
(vtkResliceImageViewer)m_imageViewer[i]->SetColorLevel(windowCenterTAGvalue[0028|1050]);
and i set following blac&white LookUpTable:
vtkLookupTable* lutbw = vtkLookupTable::New();
lutbw->SetTableRange(0,1000);
lutbw->SetSaturationRange(0,0);
lutbw->SetHueRange(0,0);
lutbw->SetValueRange(0,1);
lutbw->Build();
And images shown into my software compared with the same images shown into other software are much darker, i can not get the same effect as other DICOM viewers
My software images are right other software image is left also when i use some other LookUpTable in this example Flow i can not get the same effect (2nd row images) my image on right is much darker then other.
What i am missing why my images are darker what can i do? i was research a lot into dicom and ikt/vtk can not find good solution any help is appreciate.
Please check the values for Rescale Slope (0028,1053) and Rescale Intercept(0028,1052) and apply the Modality LUT transformation before applying the Window level.
Your dataset may have VOI LUT Function (0028,1056) attribute value of "SIGMOID" instead of "LINEAR".
I extracted the image data from one of your DICOM file (brain_009.dcm) and looked at the histogram of the image data. It looks like, the minimum value stored in the image is 0 and maximum value is 960 regardless of interpreting the data is signed or unsigned. Also, the Window Width (0028:1051) has an invalid value of “0” and you cannot use that for displaying the image.
So your default display could set the Window Width to 960 and Window Center to half the window width plus the minimum value.
Related
I have a black area around my image and I want to create a mask using OpenCV C++ that selects just this black area so that I can paint it later. How can i do that without affecting the image itself?
I tried to convert the image to grayscale and then using threshold to convert it to binary, but it affects my image since the result contains black pixels from inside the image.
Another Question : if i want to crop the image instead of paint it, how can i do it??
Thanks in advance,
I would solve the problem like this:
Inverse-binarize the image with a threshold of 1 (i.e. all pixels with the value 0 are set to 1, all others to 0)
use cv::findContours to find white segments
remove segments that don't touch image borders
use cv::drawContours to draw the remaining segments to a mask.
There is probably a more efficient solution in terms of runtime efficiency, but you should be able to prototype my solution quite quickly.
I want to create a secondary capture DICOM file as per the requirements.
I created one, but the image( pixel data in the tag 7FE0 0010 ) looks distorted. I am reading a JPEG image using Gdiplus::Bitmap and using API ::LockBits and 'btmpData.Scan0' to get the pixel data. The same is inserted into the pixel data tag - 7FE0,0010. But while viewing the same in a DICOM viewer, it is coming as distorted. The dicom tags Rows, Columns, PlannarConfiguration are updated properly. BitsAllocated, BitsStored and HighBit are given values 8,8 and 7 respectively.
While goggling I came to know that, instead of RGB format, the bits might be in the order BGR. Hence I tried to switch the bits in the place 'B' and 'R'.
But still the issue exist. Could anybody help me ?
Apparently you forgot to take into account Stride support from GDI+. An image being much more explicit than 1000 words here is what I mean:, the actual full article being here.
I have an image as shown in the inset. I sampled it in Adobe Photoshop using the blue color as the image shows. The sampled image is shown in gray-scale on the left.
I know that openCV provides a similar method to sample images that is the inRange() function. How can I find out the range of HSV values that Adobe checked for to sample my image. Since the resultant image is pretty much what I want and I am not able to determine the range myself It would be a great help if some one could guide me for the same.
You can convert your image in HSV with cv::cvtColor(...) here the documentation
Then accordingly to Wikipedia the blue is near to 240° of the HUE channel of your image.
You can set something like maxHue = 270 and a minHue = 180 or other values to scan your image.
Maybe you should set a minSaturation and a minValue to avoid the black and white.
To find the best ranges you can link them with some sliders in a Qt GUI and change them until you get the same result as photoshop...
I'm trying to merge/stitch 2 images together but found that the default stitcher class in OpenCV could not handle my images.
So I started to write my own..
Unfortunately the images are too large to attach to this message (they are both 12600x9000 pixels in size).. so I'll try to explain as good as possible.
The 2 images are not pictures takes by a camera but are tiff files extracted from a PDF file.
The images themselves were actually CAD drawings, so not much gradients in there and therefore I think the default stitcher class could not handle them.
So far, I managed to extract the features and match them.
Also I used the following well known example to stitch them together:
Mat WarpedImage;
cv::warpPerspective(img_2,WarpedImage,homography,cv::Size(2*img_2.cols,2*img_2.rows));
Mat half(WarpedImage,Rect(0,0,img_1.cols,img_1.rows));
img_1.copyTo(half);
I sort of made it fit.. because my problem is that in my case the 2 images could be aligned vertically or horizontally.
By default, all stitch examples on the internet assume the first image is the left image and the 2nd image is the right image.
So my first question would be:
How can I detect if the image is to the left, right, above or below the first image and create a proper sized new image?
Secondly..
Currently I'm getting the proper image.. however, because I'm not having some decent code to check the ideal width and height of the new image, I have a lot of black/empty space in the new image.
What would be the best C++ code to remove those black area's?
(I'm seeing a lot of Python scripts on the net.. but no C++ examples of this.. and I have 0 Python skills....)
Thank you very much in advance for your help.
Greetings,
Floris.
You can reproject the corners of the second image with perspectiveTransform. With the transformed points you can find the relative position of your image and calculate the new image size that will fit both images. This will also let you deal with the black areas, since you have the boundaries of the two images.
I am reading the official WebP lossless bitstream spec. and I have a feeling, that the document is missing some explanation.
Let me describe some fragments of the specification:
1. Introduction - clear
2. Riff header - clear
3. Transformations
The transformations are used only for the main level ARGB image: the
subresolution images have no transforms, not even the 0 bit indicating
the end-of-transforms.
Nowhere earlier was it mentioned, that the container holds some sub-resolution images. What are they? Where are they described, if not in the specification? How to they add to the final image?
Then, in the Predictor transform paragraph:
We divide the image into squares...
..what image? The main image or sub-resolution image? What if the image cannot be divided into squares (apart from pixel-size squares)?
The first 4 bits of prediction data define the block width and height
in number of bits. The number of block columns, block_xsize, is used
in indexing two-dimensionally.
Does this mean that the image width is block_xsize * block_width ?
The transform data contains the prediction mode for each block of the image.
In what way, what format?
I dont know why I am having a hard time understanding this. Maybe because I am not a native english speaker or because the description is too laconic.
I'd appreciate any help in decoding this specification :)
It was mentioned earlier. Right at the top of the document it says:
The format uses subresolution images, recursively embedded into the
format itself, for storing statistical data about the images, such as
the used entropy codes, spatial predictors, color space conversion,
and color table.
These are arrays (or a vector in the case of the color table) of data where each element applies to a block of pixels in the actual image, e.g. a 16x16 block. These "subresolution images" are not themselves subsamples of the image being compressed.
The format description calls them images because they are stored exactly like the main image is in the format. The transforms are instructions to the decoder to apply to the decompressed main image data. The entropy image is used to decompress the main image, by virtue of providing the Huffman codes for each block.