&lt;title&gt;Compression For Picture Archiving And Communication In Radiology&lt;/title&gt;

Dunham, J.G.; Hill, Rexford L.; Blaine, G. James; Snyder, Donald L.; Jost, R. Gilbert

doi:10.1117/12.935982

Cited by 7 publications

(6 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1(b), the four neighboring blocks in the horizontal, vertical, and diagonal directions (X(m, n -1), X(nz -1, n -1), X(m -1, n), X(m -1, n + 1), respectively) are utilized to predict the current block (X(m, n)). The nonlinear vector predictor can then take the form of (m, n) = W[X(nz, n -1), S(m -1, n -1), k(m-1,n),k(m-1,n+ 1)] (1) where denotes the nonlinear mapping R4A Rkl, with kl defining the number of pixels in each block. As shown in Fig.…”

Section: Nonlinear Vector Predictionmentioning

confidence: 99%

“…Medical image compression becomes increasingly important in the Picture Archiving and Communication System ( PACS) environment, which not only lowers storage space but also reduces the required time to transmit an image [1] [2]. Although discrete cosine transform (DCT) has been successfully applied to medical image compression in PACS system, vector quantization (VQ) has the advantage of high compression rate while retaining high image quality [3] [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

<title>Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis</title>

1995

View full text Add to dashboard Cite

We present a predictive learning tree-structured vector quantization technique for medical image compression. A multi-layer perceptron (MLP) based vector predictor is employed to remove first as well as higher order correlations that exist among neighboring pixels. We use a learning tree-structured vector quantization (LTSVQ) scheme, which is 1)ased on competitive learning (CL) algorithm, to encode the residual vector. LTSVQ algorithm is computationally very efficient, easy to implement and provides performance comparable to that of LBG (Linde, Buzo and Gray) algorithm. We use computerized image analysis (image segmentation) as well as mean square error (MSE) and signal-to-noise ratio (SNR) to evaluate the quality of the compressed images. We apply the neural network based predictive LTSVQ to mammographic and magnetic resonance (MR) images, and evaluate the quality of images with different compression ratios.

show abstract

Section: Nonlinear Vector Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

<title>Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis</title>

1995

View full text Add to dashboard Cite

show abstract

“…About 50 observations of this occultation were obtained in the United States (Dunham, 1983) and a preliminary reduction has given: cos6(Aa pl -Aa t ) = + .136 (A6 pl~A 6 t ) = + .057.…”

Section: The Observed 0ccultati0nsmentioning

confidence: 99%

Predictions of occultations by minor planets as a test of accuracy

Kristensen

1986

Symp. - Int. Astron. Union

View full text Add to dashboard Cite

Predictions of tracks of occultations of stars by minor planets give a strong test of the accuracy of observations, star places, and dynamical theories. A critical discussion of the 1983 September 11 occultation of 14 Piscium by (51) Nemausa shows that the systematic regional errors of fundamental stars introduce errors in the long-term predictions of occultations that are larger than relativistic effects.

show abstract

“…Image compression is a possible way to reduce the problems of storage of large data sets and long transmission time in digital imaging [1]. To reduce the redundancy within a digital image, many different compression methods have been used [2'6].…”

Section: Introductionmentioning

confidence: 99%

<title>Subjective evaluation of compressed image quality</title>

et al. 1992

View full text Add to dashboard Cite

Lossy data compression generates distortion or error on the reconstructed image and the distortion becomes visible as the compression ratio increases. Even at the same compression ratio, the distortion appears differently depending on the compression method used. Because of the nonlinearity of the human visual system and lossy data compression methods, we have evaluated subjectively the quality of medical images compressed with two different methods, an intraframe and interframe coding algorithms. The evaluated raw data were analyzed statistically to measure interrater reliability and reliability of an individual reader. Also, the analysis of variance was used to identify which compression method is better statistically, and from what compression ratio the quality of a compressed image is evaluated as poorer than that of the original. Nine X-ray CT head images from three patients were used as test cases. Six radiologists participated in reading the 99 images (some were duplicates) compressed at four different compression ratios, original, 5:1, 1 0: 1 , and 1 5 : 1 . The six readers agree more than by chance alone and their agreement was statistically significant, but there were large variations among readers as well as within a reader. The displacement estimated interframe coding algorithm is significantly better in quality than that of the 2-D block DCT at significance level 0.05. Also, 10: 1 compressed images with the interframe coding algorithm do not show any significant differences from the original at level 0.05.

show abstract

<title>Compression For Picture Archiving And Communication In Radiology</title>

Cited by 7 publications

References 12 publications

<title>Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis</title>

<title>Predictive tree-structured vector quantization for medical image compression and its evaluation with computerized image analysis</title>

Predictions of occultations by minor planets as a test of accuracy

<title>Subjective evaluation of compressed image quality</title>

Contact Info

Product

Resources

About