Say, I have a sequence on .dicom files in a folder. The cumulative size is about 100 Mb. It's a lot of data. I tried to convert data into .nrrd and .nii, but those files had the summary size of the converted .dicom files (which is fairly predictable, though .nrrd was compressed with gzip). I'd like to know, if there a file format that would give me far less sizes, or just a way to solve that. Perhaps, .vtk, or something else (not sure it qould work). Thanks in advance.
DICOM supports compression of the pixel data within the file itself. The idea of DICOM is that it's format agnostic from the point of view of the pixel data it holds.
DICOM can hold raw pixel data and also can hold JPEG-compressed pixel data, as well as many other formats. The transfer syntax tag of the DICOM file gives you the compression protocol of the pixel data within the DICOM.
The first thing is to figure out whether you need lossless or lossy compression. If lossy, there are a lot of options, and the compression ratio is quite high in some - the tradeoff is that you do lose fidelity and the images may not be adequate for diagnostic purposes. There are also lossless compression schemes - like JPEG2000, RLE and even JPEG-LS. These will compress the pixel data, but retain diagnostic quality without any image degradation.
You can also zip the files, which, if raw, should produce very good results. What are you looking to do w/ these compressed DICOMs?
Related
I am doing segmentation via deep learning in pytorch. My dataset is a .raw/.mhd format ultrasound images.
I want to input my dataset into the system via data loader.
I faced few important questions:
Does changing the format of the dataset to either .png or .jpg make the segmentation inaccurate?(I think I lost some information in this way!)
Which format is less data lossy?
How should I make a dumpy array if I don't convert the original image format, i.e., .raw/.mhd?
How should I load this dataset?
Knowing nothing about raw and mhd formats, I can give partial answers.
Firstly, jpg is lossy and png is not. So, you're surely losing information in jpg. png is lossless for "normal" images - 1, 3 or 4 channel, with 8 bit precision in each (perhaps also 16 bits are also supported, don't quote me on that). I know nothing about ultrasound images, but if they use higher precision than that, even png will be lossy.
Secondly, I don't know what mhd is and what raw means in the context of ultrasound images. That being said, a simple google search reveals some package for reading the former to numpy.
Finally, to load the dataset, you can use the ImageFolder class from torchvision. You need to write a custom function which loads an image given its path (for instance using the package mentioned above) and pass it to the loader keyword argument.
My scenario, I am trying to get Image data for upload to server. Here, I am getting huge data string length. Is there anything possibility to get short string without reducing image file resolution.
My Code for Image Data
let image = self.attachment_one_img.image
let imageData = image?.jpegData(compressionQuality: 1)
let base64String = (imageData)?.base64EncodedString(options: NSData.Base64EncodingOptions(rawValue: 0))
let trimmedString_one = base64String?.trimmingCharacters(in: .whitespaces)
Print(trimmedString_one) // I am getting huge data string length
Base-64 adds approximately 33% overhead to whatever its input data is. The only way to make it smaller is shrink your input data.
You can use other ASCII-based encodings, such as Ascii85, which have a little less overhead, but the encoded data will always be larger than the input data, because you're using fewer bits-per-byte to hold it (fewer bits-per-byte means more bytes for the same number of input bits). Since image data is typically already well compressed, it cannot be sent in significantly fewer bytes types than its data representations.
If it is JPEG data, you can reduce the quality rather than the resolution. Using a compression quality of 1 is generally excessive, and you should consider PNG rather than JPEG if you're looking for lossless compression. (JPEG is intended for photographs, and is very good at compressing them. PNG is generally better for line-art and other things with very sharp color transitions.) After ensuring your resolution is no higher than needed, tuning the quality value is the best tool for managing size.
I have a large image file (single band) that do not fit in my ram.
I wan to read it as numpy array (data) and plot it using matplotlib, possibly using imshow(data). I know how to do it for a small-sized image. But how can I do it for large file? Ofcourse, its okay to resample (possibly scipy zoom) it before plotting. But how can I resample it before reading as numpy arrray because reading of large file into memory is not possible.
maybe it is better to display the tiff with an external viewer https://superuser.com/questions/254677/what-software-works-well-for-viewing-massive-tiff-images-on-windows-7 .
Otherwise you could try to convert the tiff in an HDF5 file first (ftp://ftp.hdfgroup.org/HDF/contrib/salem/tiffutils.c) , and then load only a part of the matrix you want to display.
The question is: how Windows photo viewer generates DQ (discrete quantization) tables? And/or how any editor or application which uses IJG library generates DQ tables? I'm trying to find out the algorithm of recomputing this tables when the image is resaved and parameters with help of which it computes them.
The IJG library uses baseline set of quantization tables using the sample one in the JPEG standard (or at least used to). It then uses the "quality" parameter to scale the values in those tables. Off the top of my head, something like quality setting 75 uses the unmodified table. Quality values higher than the baseline scale those values smaller. Lower quality values scale them larger.
I have an embedded application where an image scanner sends out a stream of 16-bit pixels that are later assembled to a grayscale image. As I need to both save this data locally and forward it to a network interface, I'd like to compress the data stream to reduce the required storage space and network bandwidth.
Is there a simple algorithm that I can use to losslessly compress the pixel data?
I first thought of computing the difference between two consecutive pixels and then encoding this difference with a Huffman code. Unfortunately, the pixels are unsigned 16-bit quantities so the difference can be anywhere in the range -65535 .. +65535 which leads to potentially huge codeword lengths. If a few really long codewords occur in a row, I'll run into buffer overflow problems.
Update: my platform is an FPGA
PNG provides free, open-source, lossless image compression in a standard format using standard tools. PNG uses zlib as part of its compression. There is also a libpng. Unless your platform is very unusual, it should not be hard to port this code to it.
How many resources do you have available on your embedded platform?
Could you port zlib and do gzip compression? Even with limited resources, you should be able to port something like LZ77 or LZ88.
There are a wide variety of image compression libraries available. For example, this page lists nothing but libraries/toolkits for PNG images. Which format/library works best for you will most likely depend on the particular resource constraints you are working under (in particular, whether or not your embedded system can do floating-point arithmetic).
The goal with lossless compression is to be able to predict the next pixel based on previous pixels, and then to encode the difference between your prediction and the real value of the pixel. This is what you initial thought to do, but you were only using the one previous pixel and making the prediction that the next pixel would be the same.
Keep in mind that if you have all of the previous pixels, you have more relevant information than just the preceding pixel. That is, if you are trying to predict the value of X, you should use the O pixels:
..OOO...
..OX
Also, you would not want to use the previous pixel, B, in the stream to predict X in the following situation:
OO...B <-- End of row
X <- Start of next row
Instead you would make your prediction base on the Os.
How 'lossless' do you need?
If this is a real scanner there is a limit to the bandwidth/resolution so even if it can send +/-64K values it may be unphysical for adjacent pixels to have a difference of more than say 8 bits.
In which case you can do a start pixel value for each row and then do differences between each pixel.
This will smear out peaks but it may be that any peaks more than 'N'bits are noise anyway.
A good LZ77/RLE hybrid with bells and wwhistles can get wonderful compression that is fairly quick to decompress. They will also be bigger, badder compressors on smaller files due to the lack of library overhead. For a good, but GPLd implentation of this, check out PUCrunch