dicom vs scikit-image

Overview

scikit-image

Stacks311

Followers129

Votes12

GitHub Stars6.4K

Forks2.3K

dicom

Stacks23

Followers10

Votes0

GitHub Stars1.0K

Forks148

dicom vs scikit-image: What are the differences?

Image Format: The key difference between DICOM and scikit-image is in their image format. DICOM (Digital Imaging and Communications in Medicine) is a standard format used in the medical field to store and transmit medical images with associated patient data, while scikit-image is a Python library for image processing which typically operates on generic image formats like JPEG or PNG.
Functionality: DICOM is specifically designed for medical imaging and includes data such as patient information, acquisition parameters, and specific metadata, whereas scikit-image is a general-purpose image processing library that focuses on algorithms and techniques for manipulating and analyzing images without any specialized medical data.
Handling Medical Data: DICOM is optimized to handle medical data storage requirements, such as multi-frame images, annotations, and patient information, making it ideal for healthcare applications, whereas scikit-image is more focused on traditional image processing tasks like filtering, segmentation, and transformation.
Integration with Healthcare Systems: DICOM is widely used and supported in healthcare systems and medical devices for storing and sharing medical images, while scikit-image is commonly used in research, academia, and other non-medical applications that require image processing capabilities.
Compatibility: DICOM has specific protocols and standards for medical image exchange and interoperability, ensuring compatibility and consistency in medical imaging systems, while scikit-image may require conversion or adaptation to work with DICOM files due to its focus on generic image formats.
Community and Support: The DICOM standard is maintained by a dedicated organization and has a large community of healthcare professionals and developers supporting it, whereas scikit-image is an open-source project with a community of contributors focused on image processing techniques in various fields beyond healthcare.

In Summary, DICOM is specialized for medical imaging with extensive support for medical data, while scikit-image is a versatile image processing library with a broader application range, but lacks the specialized features of DICOM for healthcare use.

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CLI (Node.js)

Manual

Detailed Comparison

scikit-image	dicom
scikit-image is a collection of algorithms for image processing.	It is a golang DICOM image parsing library and command line tool. Its features include parsing and extracting multi-frame DICOM imagery (both encapsulated and native pixel data), exposing a Parser golang interface to make mock-based testing easier for clients etc.
Provides I/O, filtering, morphology, transformations, measurement, annotation, color conversions, test data sets, etc.;Written in Python with a well-commented source code;Has had 5,709 commits made by 116 contributors representing 29,953 lines of code;Released under BSD-3-Clause license	high-performance
Statistics
GitHub Stars 6.4K	GitHub Stars 1.0K
GitHub Forks 2.3K	GitHub Forks 148
Stacks 311	Stacks 23
Followers 129	Followers 10
Votes 12	Votes 0
Pros & Cons
Pros 6 More powerful 4 Anaconda compatibility 2 Great documentation	No community feedback yet
Integrations
No integrations available	Golang Docker

What are some alternatives to scikit-image, dicom?

Cloudinary

Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.

imgix

imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.

OpenCV

OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.

ImageKit

ImageKit offers a real-time URL-based API for image & video optimization, streaming, and 50+ transformations to deliver perfect visual experiences on websites and apps. It also comes integrated with a Digital Asset Management solution.

Cloudimage

Effortless image resizing, optimization and CDN delivery. Make your site fully responsive and really fast.

Kraken.io

It supports JPEG, PNG and GIF files. You can optimize your images in two ways - by providing an URL of the image you want to optimize or by uploading an image file directly to its API.

ImageEngine

ImageEngine is an intelligent Image CDN that dynamically optimizes image content tailored to the end users device. Using device intelligence at the CDN edge, developers can greatly simplify their image management process while accelerating their site.

FFMPEG

The universal multimedia toolkit.

GStreamer

It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing.

GraphicsMagick

GraphicsMagick is the swiss army knife of image processing. Comprised of 267K physical lines (according to David A. Wheeler's SLOCCount) of source code in the base package (or 1,225K including 3rd party libraries) it provides a robust and efficient collection of tools and libraries which support reading, writing, and manipulating an image in over 88 major formats including important formats like DPX, GIF, JPEG, JPEG-2000, PNG, PDF, PNM, and TIFF.

Related Comparisons

dicom vs scikit-image: What are the differences?

Image Format: The key difference between DICOM and scikit-image is in their image format. DICOM (Digital Imaging and Communications in Medicine) is a standard format used in the medical field to store and transmit medical images with associated patient data, while scikit-image is a Python library for image processing which typically operates on generic image formats like JPEG or PNG.
Functionality: DICOM is specifically designed for medical imaging and includes data such as patient information, acquisition parameters, and specific metadata, whereas scikit-image is a general-purpose image processing library that focuses on algorithms and techniques for manipulating and analyzing images without any specialized medical data.
Handling Medical Data: DICOM is optimized to handle medical data storage requirements, such as multi-frame images, annotations, and patient information, making it ideal for healthcare applications, whereas scikit-image is more focused on traditional image processing tasks like filtering, segmentation, and transformation.
Integration with Healthcare Systems: DICOM is widely used and supported in healthcare systems and medical devices for storing and sharing medical images, while scikit-image is commonly used in research, academia, and other non-medical applications that require image processing capabilities.
Compatibility: DICOM has specific protocols and standards for medical image exchange and interoperability, ensuring compatibility and consistency in medical imaging systems, while scikit-image may require conversion or adaptation to work with DICOM files due to its focus on generic image formats.
Community and Support: The DICOM standard is maintained by a dedicated organization and has a large community of healthcare professionals and developers supporting it, whereas scikit-image is an open-source project with a community of contributors focused on image processing techniques in various fields beyond healthcare.

dicom vs scikit-image

Overview