Search research articles

Search research articles

Related Experiment Videos

Image compression for medical imaging systems.

¹MITRE Corporation, Civil Systems Division, McLean, Virginia 22102.

Journal of Medical Systems

|June 1, 1987

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Systematic differences in validity of self-reported mammography behavior: A problem for intergroup comparisons?

Preventive medicine·1999

Same author

Capture techniques and morphological measurements of the mona monkey (Cercopithecus mona) on the island of Grenada, West Indies.

American journal of physical anthropology·1998

Same author

Group size and group composition of the mona monkey (Cercopithecus mona) on the Island of Grenada, West Indies.

American journal of primatology·1997

Same author

Left upper extremity edema, rash, and venous varicosities in a 52-year-old man.

Chest·1993

Same author

Mycotic pulmonary infections.

Critical reviews in diagnostic imaging·1992

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Digital image compression is essential for efficient storage and transmission of large medical image files. This paper explores information theory-based compression methods, focusing on accuracy for medical imaging applications.

Area of Science:

Medical Imaging
Information Theory
Computer Science

Background:

Digital images, particularly in medical imaging, are large, requiring significant storage and transmission capacity.
The need for efficient data handling arises from transmitting images over networks and long-term medical record storage.

Purpose of the Study:

To discuss image compression techniques based on information theory.
To present fundamental categories of image compression algorithms.
To highlight considerations for image quality and accuracy in medical imaging.

Main Methods:

Discussion of image compression principles rooted in information theory.
Categorization of image compression algorithms.
Analysis of factors influencing image quality and diagnostic accuracy.

Related Experiment Videos

Main Results:

Identification of two primary categories of image compression algorithms.
Emphasis on the critical role of information theory in developing compression strategies.
Consideration of trade-offs between compression ratio, image quality, and diagnostic accuracy.

Conclusions:

Image compression is vital for managing large digital image datasets in medical contexts.
Understanding information theory provides a foundation for effective compression algorithm design.
Maintaining high image quality and accuracy is paramount for medical diagnostic purposes.