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  1. Stackups
  2. Application & Data
  3. Image Optimization
  4. Image Processing And Management
  5. CImg vs dicom

CImg vs dicom

OverviewComparisonAlternatives

Overview

CImg
CImg
Stacks1
Followers6
Votes0
dicom
dicom
Stacks23
Followers10
Votes0
GitHub Stars1.0K
Forks148

CImg vs dicom: What are the differences?

Introduction

In the realm of medical image processing, two key tools are CImg and DICOM. Below are the key differences between the two.

  1. File formats: CImg is primarily designed to handle bitmap images, while DICOM specializes in managing medical imaging data stored in the DICOM format. CImg supports a wide range of image formats, including common ones like JPEG, PNG, BMP, etc., whereas DICOM focuses specifically on medical imaging data formats for interoperability in healthcare settings.

  2. Usage: CImg is more geared towards general image processing tasks such as filtering, resizing, and transformations, while DICOM is tailored for medical image analysis and visualization in fields like radiology and cardiology. CImg serves as a versatile tool for basic image manipulation, while DICOM caters to the specialized needs of healthcare professionals dealing with medical images.

  3. DICOM metadata: In the DICOM standard, metadata plays a crucial role as it contains essential information about the patient, imaging device, acquisition parameters, etc. CImg does not have dedicated support for managing DICOM metadata, while DICOM provides robust functionality for handling and extracting metadata for accurate interpretation and analysis in a clinical context.

  4. Pixel data: CImg is more focused on pixel-level manipulation and processing of images, offering a range of functions for pixel value access and modification. On the other hand, DICOM emphasizes maintaining the integrity and consistency of pixel data acquired from medical imaging modalities, ensuring accurate representation and preservation of clinical information.

  5. Medical image annotations: DICOM includes extensive support for medical image annotations, allowing users to add clinical information, annotations, and measurements directly to the image data. CImg, being more generic in nature, lacks specific features for managing medical annotations and related data that are crucial for diagnostic purposes in healthcare settings.

  6. Compatibility with medical systems: DICOM is widely used in healthcare institutions and medical systems for storing, transmitting, and managing medical images, ensuring compliance with industry standards for seamless interoperability. In contrast, CImg is not optimized for integration with medical systems, making it less suitable for applications requiring adherence to stringent healthcare regulations and workflows.

In Summary, the key differences between CImg and DICOM lie in their focus on file formats, usage, handling of DICOM metadata, pixel data processing, medical image annotations, and compatibility with medical systems.

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Detailed Comparison

CImg
CImg
dicom
dicom

It mainly consists in a (big) single header file CImg.h providing a set of C++ classes and functions that can be used in your own sources, to load/save, manage/process and display generic images.

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.

C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android; Usage ranges from interactive art, to mines inspection, stitching maps on the web or through advanced robotics
high-performance
Statistics
GitHub Stars
-
GitHub Stars
1.0K
GitHub Forks
-
GitHub Forks
148
Stacks
1
Stacks
23
Followers
6
Followers
10
Votes
0
Votes
0
Integrations
C++
C++
Python
Python
Linux
Linux
biicode
biicode
Java
Java
Windows
Windows
Raspbian
Raspbian
Golang
Golang
Docker
Docker

What are some alternatives to CImg, dicom?

Cloudinary

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

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

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

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

Cloudimage

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

scikit-image

scikit-image

scikit-image is a collection of algorithms for image processing.

Kraken.io

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

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

FFMPEG

The universal multimedia toolkit.

GStreamer

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.

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