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Observer performance for JPEG vs. Wavelet image compression of x-ray coronary angiograms

Craig A. Morioka, Miguel P. Eckstein, Jay L. Bartroff, Jöerg Hausleiter, Gal Aharanov, and James S. Whiting

Open Access Open Access

Abstract

Development of “filmless” cardiac catheterization laboratories is eminent. The problems of implementing a digital catheterization laboratory involve archiving large amounts of data per procedure and high transfer rates to retrieve previous procedures. Lossy compression can accommodate these changes, but at the cost of possibly impairing detection of clinically important angiographic features. Our study involves the observer detection and classification of features in clinical images and the effects that JPEG and wavelet compression have on the detectability of these features. We found no significant degradation in human observer performance with 7:1 and 15:1 JPEG compressed images in 6 clinically relevant visual tasks. Human observer performance for wavelet compression degraded significantly for 2 out of 6 tasks at 7:1 compression and 4 out of 6 tasks at 19:1 compression.

©1999 Optical Society of America

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Supplementary Material (1)

Media 1: MOV (317 KB)     

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Figures (6)
Fig. 1.
Fig. 1. Dynamic demonstration of creation of test-images used for psychophysical experiments (test- images shown are 400×338 pixel subsets of the 512×512 images used in the experiments) [Media 1]

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Fig. 2.
Fig. 2. -C. Example of thrombosis cropped to 150×150 pixels from original 512×512 pixel image: a) uncompressed, b) 14.9:1 jpeg, c) 19.2:1 wavelet. Arrow indicates location of thrombosis.

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Fig. 3.
Fig. 3. -C. Example of ulcerated plaque: a) uncompressed, b) 14.9:1 JPEG, c) 19.2:1 wavelet. Arrow indicates location of ulcerated plaque.

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Fig. 4.
Fig. 4. -c. Example of bridging lumen: a) uncompressed, b) 14.9:1 jpeg, c) 19.2:1 wavelet. Arrow indicates location of bridging lumen.

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Fig. 5.
Fig. 5. -C. Example of grading stenosis: a) uncompressed, b) 14.9:1 JPEG, c) 19.2:1 Wavelet. Arrow indicates location of stenosis.

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Fig. 6.
Fig. 6. Human performance for 6 different observers. Upper Left: lumen detection. Upper Right: graded stenosis. Middle Left: thrombosis detection. Middle Right: ulcer detection Bottom Left: 16 frames/sec stenosis grading. Bottom Right: 16 frames/sec thrombosis detection.

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Tables (2)

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Table 1. Visual tasks and display conditions investigated in the present study

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Table 2. Results of observer performance for various detection tasks (α=0.05).

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Equations (8)

Equations on this page are rendered with MathJax. Learn more.

I ( x , y ) = P ( x , y ) + S ( x , y )
P ( x , y ) = I o e − D p ( x , y ) e − D a ( x , y )
D ( p ) = ∑ i = 1 N μ i t i
P s ( x , y ) = P ( x , y ) e − D s ( x , y )
I s ( x , y ) = [ I ( x , y ) − S ( x , y ) ] e − D s ( x , y ) + S ( x , y )
S ( x , y ) = I ( x , y ) * α h ( x , y ) + β
h ( x , y ) = 20 e − A ∣ x − x o ∣ e − B ∣ y − y o ∣
A = B = 2 log ( 2 ) ⁄ 75 .
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