brainweb | Create pages with Brainfuck globe_with_meridians | Compiler library

by paulohenriquesn JavaScript Version: server License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | brainweb Summary

brainweb is a JavaScript library typically used in Utilities, Compiler applications. brainweb has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Create WebPages(HTML) In BrainFuck.

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Support

brainweb has a low active ecosystem.

It has 18 star(s) with 0 fork(s). There are no watchers for this library.

It had no major release in the last 12 months.

brainweb has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of brainweb is server

Quality

brainweb has 0 bugs and 0 code smells.

Security

brainweb has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

brainweb code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

brainweb does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

brainweb releases are available to install and integrate.

brainweb saves you 17 person hours of effort in developing the same functionality from scratch.

It has 49 lines of code, 1 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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brainweb Key Features

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brainweb Examples and Code Snippets

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Community Discussions

Trending Discussions on brainweb

What is the best measurement for validating a denoising function in Image processing? Signal to Noise ratio seems to fail me

Convert BrainWeb (.mnc) to MetaImage (.mha) format for TumorSim

Binarize image data

QUESTION

What is the best measurement for validating a denoising function in Image processing? Signal to Noise ratio seems to fail me

Asked 2020-Oct-26 at 13:32

I'm using BrainWeb a simulated dataset for normal brain MR images. I want to validate MyDenoise function which calls denoise_nl_means of skimage.restoration package. To do so, I downloaded two sets of images from BrainWeb, a original image with 0% noise and 0% Intensity non-uniformity, and a noisy image with the same options but 9% noise and 40% Intensity non-uniformity. And, I calculate Signal To Noise ratio (SNR) based on a deprecated version of scipy.stats as follows:

...

ANSWER

Answered 2020-Oct-26 at 13:32

This is not how you calculate SNR.

The core concept is that, for any one given image, you don’t know what is noise and what is signal. If we did, denoising wouldn’t be a problem. Therefore, it is impossible to measure the noise level from one image (it is possible to estimate it, but we cannot compute it).

The solution is to use that noise-free image. This is the ground truth, the objective of the denoise operation. We can thus estimate the noise by comparing any one image to this ground truth, the difference is the noise:

Source https://stackoverflow.com/questions/64537849

QUESTION

Convert BrainWeb (.mnc) to MetaImage (.mha) format for TumorSim

Asked 2019-Oct-17 at 17:05

I want to convert .mnc files from BrainWeb (https://brainweb.bic.mni.mcgill.ca/brainweb/anatomic_normal_20.html) to .mha file format for use in TumorSim (https://www.nitrc.org/projects/tumorsim/).

I have tried converting the file from .mnc to .nii using nibabel and mnc2nii, and then converting the .nii file to the .mha format.

However, this process leads to the file size increasing dramatically (from 56.9 MB .mha to 56.9~227.5 MB .nii depending on output voxel format)

From there, converting the .nii file to the .mha format retains the same file size. The size of .mha files used in TumorSim are around 4.8 MB.

Objective: I want a 1 step solution to convert .mnc files to .mha files Code: ...

ANSWER

Answered 2019-Oct-15 at 14:10

It looks like you might have a permissions or path problem. SimpleITK can't seem to find the file. Try checking the permissions and put in a full path name.

Here's a little test program I wrote to check the MNC IO:

Source https://stackoverflow.com/questions/58370818

QUESTION

Binarize image data

Asked 2018-Oct-24 at 11:04

I have 10 greyscale brain MRI scans from BrainWeb. They are stored as a 4d numpy array, brains, with shape (10, 181, 217, 181). Each of the 10 brains is made up of 181 slices along the z-plane (going through the top of the head to the neck) where each slice is 181 pixels by 217 pixels in the x (ear to ear) and y (eyes to back of head) planes respectively.

All of the brains are type dtype('float64'). The maximum pixel intensity across all brains is ~1328 and the minimum is ~0. For example, for the first brain, I calculate this by brains[0].max() giving 1328.338086605072 and brains[0].min() giving 0.0003886114541273855. Below is a plot of a slice of a brain[0]:

I want to binarize all these brain images by rescaling the pixel intensities from [0, 1328] to {0, 1}. Is my method correct?

I do this by first normalising the pixel intensities to [0, 1]:

...

ANSWER

Answered 2018-Mar-10 at 18:47

Have you tried a threshold on the image?

This is a common way to binarize images, rather than trying to apply a random binomial distribution. You could try something like:

Source https://stackoverflow.com/questions/49210078

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

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Install brainweb

You can download it from GitHub.

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