I got a CD from the hospital that is a head CD scan.
I am completely new to medical imaging. What I would like to do perform a volume rendering of the CT scan.
It is in DICOMDIR format. How and where would I start?
From messing about with various tools I get the feeling that I need to extract each series into DICOM format. Is this correct and if so how would I do it?
Unless you were given the volume data your rendering will be disappointing at best. Many institutions still acquire head CT's in separate "step-slices", and not as volumes so here you will have significant 'stepping' artifact.
Even if it was acquired with volume data, unless they transferred all the data to your CD, you will still be stuck with only the processed 'slab' or 'slice' images.
The best way to do a volume rendering is to actually have the volume data. "Slice image" data has most of the information dumbed down and removed. You are just getting 20 or 30 images in 256 x 256 x (8 or 16 bit greyscale) array data.
If you have a mac try OsiriX - it's free, open source and will do everything you need and more. If you don't, and this is a one time thing, you could always sign up for a free demo of a commercial grade DICOM viewer. Medical image viewing software is insanely expensive and would be impossible to sell without demos. Just claim to be working for a clinician and you'll have no problem getting working software.
I believe ImageJ will open any of the files in the DICOMDIR for you. I'm not entirely sure it can open the entire study from the DICOMDIR, but I'm fairly certain it will handle any individual files you need to open. It should also offer the option to export the images to various other formats. If you need more info, feel free to post a comment.
You can also try MevisLab (http://www.mevislab.de/) it is free but a bit more complex to use and maybe it requires two steps to get the rendering of your dicom images.
Most probably you have to use one of the widgets they provide to convert the image and then to load the converted image and render it.
I have done with ImageJ but ImageJ not support compress dicom files at that time you have create your own logic to read compress dicom file.
Fiji and VolumeJ are also Good Option for Volume Rendering
Try Real3d VolViCon which is an advanced application for reconstruction of computed tomography (CT), magnetic resonance (MR), ultrasound, and x-rays images. It gives features for exporting 3D surfaces or volume as triangular mesh files for creating physical models using 3D printing technologies. It also provides high-quality visualization, linear and angular measurement tools, and various type of markups. It takes a single raw volume file or a sequence of 2D (i.e., DICOM) files and reconstructs 3D volume (voxels) and mesh (surfaces) models.
Related
VkImageCreateInfo has the following member:
VkFormat format;
And VkImageViewCreateInfo has the same member.
What I don't understand why you would ever have a different format in the VkImageView from the VkImage needed to create it.
I understand some formats are compatible with one another, but I don't know why you would use one of the alternate formats
The canonical use case and primary original motivation (in D3D10, where this idea originated) is using a single image as either R8G8B8A8_UNORM or R8G8B8A8_SRGB -- either because it holds different content at different times, or because sometimes you want to operate in sRGB-space without linearization.
More generally, it's useful sometimes to have different "types" of content in an image object at different times -- this gives engines a limited form of memory aliasing, and was introduced to graphics APIs several years before full-featured memory aliasing was a thing.
Like a lot of Vulkan, the API is designed to expose what the hardware can do. Memory layout (image) and the interpretation of that memory as data (image view) are different concepts in the hardware, and so the API exposes that. The API exposes it simply because that's how the hardware works and Vulkan is designed to be a thin abstraction; just because the API can do it doesn't mean you need to use it ;)
As you say, in most cases it's not really that useful ...
I think there are some cases where it could be more efficient, for example getting a compute shader to generate integer data for some types of image processing can be more energy efficient than either float computation or manually normalizing integer data to create unorm data. Using aliasing you the compute shader can directly write e.g. uint8 integers and a fragment shader can read the same data as unorm8 data
I'm using two custom push filters to inject audio and video (uncompressed RGB) into a DirectShow graph. I'm making a video capture application, so I'd like to encode the frames as they come in and store them in a file.
Up until now, I've used the ASF Writer to encode the input to a WMV file, but it appears the renderer is too slow to process high resolution input (such as 1920x1200x32). At least, FillBuffer() seems to only be able to process around 6-15 FPS, which obviously isn't fast enough.
I've tried increasing the cBuffers count in DecideBufferSize(), but that only pushes the problem to a later point, of course.
What are my options to speed up the process? What's the right way to do live high res encoding via DirectShow? I eventually want to end up with a WMV video, but maybe that has to be a post-processing step.
You have great answers posted here to your question: High resolution capture and encoding too slow. The task is too complex for the CPU in your system, which is just not fast enough to perform realtime video encoding in the configuration you set it to work.
Updated question to be less vague.
I plan to log sensor data by time so something like sqlite would be perfect, but it requires too much resources in something like an atmega328p. Most of the searching will be done off the uC.
What do other people use? Flat text files? XML? A more complicated data structure?
Thanks for the feedback. It is good to know what other people are using. I've decided to serialize my data structures and save them in a binary file to eliminate string processing on the uC for now.
I've used flat text files for similar projects, albiet years ago, but I believe it's still a good approach for that environment. Since you don't need to process the data on-chip, you want it to be as efficient as possible (as little overhead as possible).
However, if you want more flexibility and weren't as concerned about space, perhaps saving JSON objects would be better, where each field is identified clearly. A tiny bit of overhead for creating the objects, but allows you to add and remove fields without complex logic on the interpreting side. I would pick JSON over XML just because you have about half the overhead (in space, and probably in processing).
With a small micro-controller like the 328, it is very important to determine the space requirements.
How big is each record? How many records do you want to store? How will you get the records off of the micro-controller?
Like Doug, I usually use a flat text to store data. So each record might contain year, day of the year and a value if I am storing a value once a day.
The file would look like:
11,314,100<cr>
11,315,99<cr>
11,316,98<cr>
11,317,220<cr>
You could store approximately 90-100 records, requiring you to dump the data every three months
If you need more then the 1kEEprom holds (200 5 byte records, 100 10 byte records or simliar) then you will need additional memory using an IC, SD or Flash.
If you want to unplug the memory and plug it into a PC, then the SD or Flash would be best.
You could use a vinculum chip from FTDIChip.com to simplify writing the fat files to flash drive.
I want to convert a sound from Mic to binary and match it from the database(a type of voice identification program but don't getting idea how to get sound from Mic directly so that i can convert it to binary?Also it is possible or not. Please guide me )
See this:
http://www.dotnetspider.com/resources/4967-How-record-voice-from-microphone.aspx
You're not going to be able to identify voices by doing a binary comparison on sound data. The binary of a particular sound will not be identical to an imitation of that sound unless it is literally the same file because of minor variations in just about everything. You'll need to do some signals processing to do a fuzzy comparison of the data. You can read about signal processing on wikipedia.
You will probably find it easier to use a third party library to process the sound for you. Something like this might be a good start.
You're looking at two very distinct problems here.
The first is pretty technical: Getting sound from the microphone into a digital waveform. How you do this exactly depends on the OS and API you're using (on Windows, you're probably looking at DirectX audio or, if available, ASIO). Typically, this is how you'd proceed:
Set up a recording buffer for the microphone, with suitable parameters (number of channels, physical input on the sound card, sample rate, bit depth, buffer size)
Start the recording. This usually involves pointing the sound library to a callback function to process the recorded buffer.
In the callback, read the buffer, convert it to a suitable format, and append it to the audio file of your choice. (You could also record to RAM only, but longer recordings may exceed available storage).
Store the recorded audio in a suitable database field (some kind of binary blob)
This is the easy part though; the harder part is matching a chunk of audio data against other chunks. A naïve approach would be to try and find exact matches, but that won't help you much, because the chance that you find one is practically zero - recording equipment, even the best, introduces a bit of random noise, and recording setups vary slightly whether you want to or not, so even if you'd have someone say something twice, perfectly identical, you'd still see differences in the recorded audio.
What you need to do, then, is find certain typical characteristics of the waveform. Things you could look for are:
Overall amplitude shape
Base frequencies
Selected harmonics (formants)
Extracting these is non-trivial and involves pretty severe math; and then you'll have to condense them into some sort of fingerprint, and find a way to compare them with some fuzziness (so that a near-match is good enough, rather than requiring exact matches). Finding the right parameters and comparison algorithms isn't easy, and it takes a lot of tweaking and testing; your best bet is to go find a library that does this for you.
Say you have a conference room and meetings take place at arbitrary impromptu times. You would like to keep an audio record of all meetings. In order to make it as easy to use as possible, no action would be required on the part of meeting attenders, they just know that when they have a meeting in a specific room they will have a record of it.
Obviously just recording nonstop would be inefficient as it would be a waste of data storage and a pain to sift through.
I figure there are two basic ways to go about it.
Recording simply starts and stops according to sound level thresholds.
Recording is continuous, but split into X minute blocks. Blocks found to contain no content are discarded.
I like the second way better because I feel there is less risk for losing data because of late starts, or triggers failing.
I would like to implement in Python, and on Windows if possible.
Implementation suggestions?
Bonus considerations that probably deserve their own questions:
best audio format and compression for this purpose
any way of determining how many speakers are present, assuming identification is unrealistic
This is one of those projects where the path is going to be defined more about what's on hand for ready reuse.
You'll probably find it easier to continuously record and saving the data off in chunks (for example, hour long pieces).
Format is going to be dependent on what you in the form of recording tools and audio processing library. You may even find that you use two. One format, like PCM encoded WAV for recording and processing, but compressed MP3 for storage.
Once you have an audio stream, you'll need to access it in a PCM form (list of amplitude values). A simple averaging approach will probably be good enough to detect when there is a conversation. Typical tuning attributes:
* Average energy level to trigger
* Amount of time you need to be at the energy level or below to identify stop and start (I recommend two different values)
* Size of analysis window for averaging
As for number of participants, unless you find a library that does this, I don't see an easy solution. I've used speech recognition engines before and also done a reasonable amount of audio processing and I haven't seen any 'easy' ways to do this. If you were to look, search out universities doing speech analysis research. You may find some prototypes you can modify to give your software some clues.
I think you'll have difficulty doing this entirely in Python. You're talking about doing frequency/amplitude analysis of MP3 files. You would have to open up the file and look for a volume threshold, then cut out the portions that go below that threshold. Figuring out how many speakers are present would require very advanced signal processing.
A cursory Google search turned up nothing for me. You might have better luck looking for an off-the-shelf solution.
As an aside- there may be legal complications to having a recorder running 24/7 without letting people know.