do you know anybody if it is possible to get some model file from doctor when he made 3d ultrasound of pregnant woman? I mean something like DICOM (.dcm) file or .stl file or something like that what I can then work with and finaly print with 3D printer.
Thanks a lot.
Quick search for "dicom 3d ultrasound sample" resulted in one that you might be able to use for internal testing. You can get the file from here
Bonjour,
The first problem you will face is the file format.
Because of the way the images are generated, 3D ultrasound data have voxels that are expressed in a spherical system. DICOM (as it stand now) only support voxels in a Cartesian system.
So the manufacturers have a few choices:
They can save the data in proprietary format (ex: Kretzfile for Ge, MVL for Samsung).
They can save the data in private tags inside a DICOM file (Ge, Hitachi, Philips)
They can re-format the voxels to be in Cartesian, but then the data has been transformed and nobody like that. And anyway, since they also need to save the original (untransformed) data, the companies that do offer Cartesian voxels, usually save them in the same way as the original, so they are not saved in normal DICOM tags, but in their proprietary version.
This is why most of the standard software that can do 3D from CT or MR will not be able to cope with the data files.
Then the second problem is the noise. Ultrasound datasets are inherently very noisy! Again standard 3D reconstruction software where designed for CT or MR and have problems with this.
I do have a product that will read most of the 3D ultrasound files and create an STL model directly from the datasets (spherical or Cartesian). It is called baby SliceO (http://www.tomovision.com/products/baby_sliceo.html)
Unfortunately, it is not free, but you can try it without any licenses. Give it a try and let me know if you like it...
Yves
Related
As I am working on my project that is to detect FOD (Foreign Object Debirs) that is found on the runway. FOD include anything like nuts, bolts, screws, locking wires, plastic debris, stones etc. that has the potential to cause damage to the aircraft. Now I have searched on the Internet to find any image dataset but no dataset is available related to FOD. Now my question is kindly guide me that how can I make my own dataset of images that can then be used for training purpose.
Kindly guide me in making image dataset for both classification and detection purposes. And also the data pre-processing that will be required. Thanks and waiting for the reply!
Although the question is a bit vague regarding your requirements and the specs of your machine, I'll try to answer it. You'll need object detection to do your task. There are many models available which you can use like Yolo, SSD, etc..
To create your own dataset, you can follow these steps:
Take lots of images of your objects of interest in various conditions, viewpoints and backgrounds. (Around 2000 per class should be good enough).
Now annotate (or mark) where your object is in the image. If you're using Yolo, make use of Yolo-mark for annotating. There should be other similar tools for SSD and other models.
Now you can begin training.
These steps should get you started or at least point you in the right direction.
You can build your own dataset with this code. I wrote it, and it works correctly.
You need to import the libraries and add your DATADIR.
if __name__ == "__main__":
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path,img))
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
for features, label in training_data:
x_train.append(features)
y_train.append(label)
#create pikle
pickle_out = open("x_train.pickle", "wb")
pickle.dump(x_train, pickle_out)
pickle_out.close()
pickle_out = open("y_train.pickle", "wb")
pickle.dump(y_train, pickle_out)
pickle_out.close()
In case if you're starting completely from scratch, you can use "Dataset Directory", available on Play store. The App helps you in creating custom datasets using your mobile. You'll have to sign in to your Google drive such that your dataset is stored in Drive rather on your mobile. Additionally, It also contains Labelling the entity for classification and Regression predictive models.
Currently, the App supports Binary Image Classification and Image Regression.
Hope this Helped!
Download Link :
https://play.google.com/store/apps/details?id=com.applaud.datasetdirectory
So I want to be able to convert a .wav file to a json table using lua, which would probably include something like {time="0:39.34",hz=440} or something. I already have all my json libraries but I just need a method to be able to convert a .wav file into something that I could use to convert it into json. If there's already a library that can do this then I need the source code of the library to be able to implement it into my code for a single-file program.
At each point in wav you'll have a full spectrum, not just "the hertz frequency". You'll have to perform a fourier transform on the data, and from many peaks in spectrum select the one you're interested in - be it fundamental or dominant, etc.
There are libs for Fast Fourier Transform out there, like LuaFFT, but you'd better get more clear picture of what you really need from the WAV. If you're just trying to read DTMF signal, you don't really need the full scale spectrum analysis.
I want to use NiftyNet to implement Deep Learning on medical image processing. However, there is one thing I haven't figured out regarding the data input: how does it join the multi-modality images? I saw the demo of BRATS2017, they seems to use 4 different modalities, and in the configuration file, they just included the directory of the images and they claim it will "concatenate" the images. But I want to know more, as those images are 3D, how are they concatenated? [slice1-30]:[slice1-30].. or [slice1, slice1, slice1 ...]:[slice2, slice2, slice2...]?
And can we control the data organization part? If so, which file should I modify?
Any suggestion would be greatly appreciated!
In this case, the 3D images are concatenated in an additional dimension. You control the order they're concatenated in by specifying the order of files to load in the *.ini files.
However, as long as you're consistent, it shouldn't matter what order the modalities go in.
The images are concatenated in the channel dimension. For 2D images, the dimensions are NSSC: batch size, 2 spatial dimensions, then channel. For 3D images, the dimensions are NSSSC: batch size, 3 spatial dimensions, then channel.
I would like to perform a binary classification of documents (.txt, .pdf, .jpeg, .img, etc.) into two categories: printable and non-printable. Essentially our school runs a free printing service for clubs, but the reality is that many clubs abuse the free printing and end up printing their homework, papers, etc., which amounts to thousands of dollars in ink and paper. Thus we would like to take some unsupervised methods to help limit this by determining whether a document is with high probability not club related (e.g. Biophysics paper, there is no biophysics club!).
So this is a very simple binary classification problem. I am not looking for low-level implementation details or which ML algorithms I should use, but rather how I should discover the relevant features that will then be fed to the training, etc.
My first idea was to gather all the documents that students print in the library. The idea is that if you have actual club printing, you'll do it for free at the club printing center rather than pay for it at the library. That would be a massive dataset, assuming every document printed at the library is assigned the non-printable/club material category. Unfortunately, the school is very liberal and opposed to allowing this due to privacy concerns, so it is not really an option without legal risks.
A similar-minded option would be to collect documents that are tied to courses / school work, e.g. course syllabi, available course documents online (homeworks, papers, etc.) and do feature extraction / selection on these. The assumption is that students would be abusing the printing to generally print material relevant to their studies.
While for .pdf and .txt based document this approach should have reasonable performance, I am at a loss at how to classify image based documents, besides perhaps using the title of the document and other meta data. A clever violator could simply convert all their text documents to image format to circumvent this system. However that is outside the scope of this question and should be saved for a future question / research. For now the scope is just text based documents.
Note that there are previous questions on topics similar to this, but mine is very specific and I believe it may pose challenges that something like movie review classification might not have to face.
I just wanted to leave a comment but it ended way longer than what I imagined.
While this is an interesting problem I'm not sure ML will get you what you need easily.
Firstly your classification problem is of the type A vs the World and A isn't strictly defined. Unless you know exactly what kind of stuff the clubs print you can't really say that new material belong or no to that class.
This will prove particularly difficult when you will need to assemble a large enough training set to be able to cover whatever can or cannot be printed. Such task will be extremely tedious, and as you said you won't have access to what the clubs usually print out so at best you will have a large class imbalance in your training set.
As the goal is to make the system automated (I mean if there is human interaction anyway, it's faster to check what will be printed than to make a ML algorithm that will provide a score that a human will have to investigate anyway) the number of false positives and false negatives will also be problematic. There will be cases where the clubs won't be able to print things they have the right to.
As you said you could simplify greatly the problem by classifying Course Material and Not Course Material. For that I will look towards BoW because some words are more present than others in papers or course material (everything remotely technical). The number of words as well as the overall size of the file seem like sensible things to extract. The structure is often also particular : it might be a good idea to extract such things : "number of lines with less than x words", "number of lines per page", "number of pictures" (if that's something you can extract from the file), ...
For pictures the major thing to check would be if this a scan of something (often they will scan and print course related things I guess), for that the format of the image is already a good indication but I don't see other things that would be particularly "course related".
So for me, if you can't really define precisely one of your two classes don't go with classification or reduce the problem to something you can really define (course related things).
If you are able to compile a "black list" of documents students are not allowed to print, you can then implement a several layers rejection mechanism.
I would suggest these 3 levels:
compare the md5 of the file they want to print with a database of all the md5 of the black-listed documents.
if the 1) is passed, compare repeat 1) but at a page level, rather than at document level (perhaps they want to print just few pages rather than the entire document).
if 2) is passed you can compare the page they want to print with the pages of the black-listed documents document using an image similarity method, like SSIM. if you get a high score between the page they want print and one of the black-listed items do not print, and update your md5 database accordingly.
if 3) is passed: print!
A few words about SSIM: this method is quite robust to noise, so even a smart student who added some sort of niose to the image will be caught
However:
you have to find a proper way to extract a region of interest (ROI) from the page and the db of documents (if the two ROIs are in two different area of the page, SSIM will be negative)
SSIM might be slow! definitely a C implementation is needed here.
I think SSIM is not rotational invariant, hence the check will fail if they print the page upside down (unless you have a smart way to rotate the page).
I am using Metaio's Creator to create an AR event and using a model the client purchased from TurboSquid.com. Everytime I try to convert the .3DS file to an .MD2 file I get an error that there are to many polygons.
Is there a program that can automatically convert the .3DS or .OBJ to an .MD2 without lowering the polygon count or automatically taking polygons out without risking the integrity of the model?
MD2 only supports 4096 polygons inherently. As #0r10n said, you have to reduce the number of polygons to make it working with MD2. For conversion, I had the best experience using the QTip plugin for 3Ds Max here: http://qtipplugin.com/
Very easy to use and very powerful.
If the model has too many polygons you can import it into a 3DCC-tool like Max, Maya or Blender and use their tools to reduce the polygon-count.
For example using Blender 2.49 you can use the PolyReducer-Script, that preserves UV-Coordinates.