Literature DB >> 12750612

Evaluation of Lossy data compression of chest X-rays: a receiver operating characteristic study.

Elmar Kotter1, Arnold Roesner, Jan Torsten Winterer, Nadir Ghanem, Almut Einert, Dieter Jaeger, Peter Uhrmeister, Mathias Langer.   

Abstract

RATIONALE AND
OBJECTIVES: To evaluate the quality of chest radiographs after 32:1 compression/decompression with different image compression algorithms.
METHODS: Ten digital (Thoravison) radiographs of an anthropomorphic chest phantom with superimposed simulated nodular lesions (NL) and linear reticular lesions (LL) were obtained. Each radiograph was subdivided into 15 fields; they contained the lesions with a probability of 0.5. The radiographs were compressed and decompressed by using JPEG, fractal and wavelet algorithms at a compression rate of 32:1. Five radiologists evaluated the images. Data were analyzed with the receiver operating characteristic (ROC) method (comparison of area under curve).
RESULTS: At 32:1 JPEG or wavelet compression, no statistically significant difference was observed for both NL and LL when compared with the original images. The fractal algorithm performed significantly lower for both NL and LL when compared with the original radiographs.
CONCLUSION: The JPEG and wavelet image compression does not result in loss of relevant information for chest x-rays at a compression rate of 32:1.

Entities:  

Mesh:

Year:  2003        PMID: 12750612     DOI: 10.1097/01.RLI.0000057032.41715.15

Source DB:  PubMed          Journal:  Invest Radiol        ISSN: 0020-9996            Impact factor:   6.016


  2 in total

1.  Technologies for image distribution in hospitals.

Authors:  Elmar Kotter; Tobias Baumann; Dieter Jäger; Mathias Langer
Journal:  Eur Radiol       Date:  2006-03-10       Impact factor: 5.315

2.  Use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients.

Authors:  Ronilda C Lacson; Bowen Baker; Harini Suresh; Katherine Andriole; Peter Szolovits; Eduardo Lacson
Journal:  Clin Kidney J       Date:  2018-07-03
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.