A statistical method for evaluation quality of medical images: a case study in bit discarding and image compression

Chen, Tzong Jer; Chuang, Keh Shih; Chiang, Yuang Chin; Chang, Jen Hao; Liu, Ru‐Shi

doi:10.1016/j.compmedimag.2004.01.003

Cited by 16 publications

(30 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We define the peak ratio as the ratio of highest peak values in the Z histograms between the manipulated and original images. 14 …”

Section: Z Histogram and Peak Ratiomentioning

confidence: 99%

“…This Z histogram had been proven to correspond well to the image variation in spatial properties. 14,15 Spatial correlation increases with image blurring and accompanies increase in Z value. This Z value may increase at higher Z value areas to form a peak.…”

Section: Z Histogram and Peak Ratiomentioning

confidence: 99%

“…For lossy wavelet image compression, the Moran peak ratio corresponds well to quality degradation of medical images. 14 In this work, we use Moran statistics for the quantitative blurring measurement in digital images. In the following, we first introduce the Moran statistics and peak ratio of histograms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Blurring Index for Medical Images

Chen¹,

Chang

Shiao³

et al. 2005

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

This study was undertaken to investigate a useful image blurring index. This work is based on our previously developed method, the Moran peak ratio. Medical images are often deteriorated by noise or blurring. Image processing techniques are used to eliminate these two factors. The denoising process may improve image visibility with a trade-off of edge blurring and may introduce undesirable effects in an image. These effects also exist in images reconstructed using the lossy image compression technique. Blurring and degradation in image quality increases with an increase in the lossy image compression ratio. Objective image quality metrics [e.g., normalized mean square error (NMSE)] currently do not provide spatial information about image blurring. In this article, the Moran peak ratio is proposed for quantitative measurement of blurring in medical images. We show that the quantity of image blurring is dependent upon the ratio between the processed peak of Moran's Z histogram and the original image. The peak ratio of Moran's Z histogram can be used to quantify the degree of image blurring. This method produces better results than the standard gray level distribution deviation. The proposed method can also be used to discern blurriness in an image using different image compression algorithms.KEY WORDS: Moran peak ratio, image blurring, image quality

show abstract

“…We define the peak ratio as the ratio of highest peak values in the Z histograms between the manipulated and original images. 14 …”

Section: Z Histogram and Peak Ratiomentioning

confidence: 99%

Section: Z Histogram and Peak Ratiomentioning

confidence: 99%

See 1 more Smart Citation

A Blurring Index for Medical Images

Chen¹,

Chang

Shiao³

et al. 2005

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Sarnoff just noticeable differences (JND) vision model is being used successfully to predict digital-video quality [7]. The JND metrics is a computational model that simulates known physiological mechanisms in the human visual system, including the contrast sensitivity of the eye, luminance, spatial frequency and orientation responses of the visual cortex [5]. The success of these metrics is in some sense heuristic and developed as an ISO standard (ISO 20462) [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the metrics combined perceptual quality measurement and human visual system (HVS) features [4,5] has been developed. Since a human observer is the end user of image quality measurement, the metrics used for assessing the image quality should take into account the impact of HVS [6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Medical Image Quality Index

Lin¹,

Lin

Feng

et al. 2010

J Digit Imaging

Self Cite

View full text Add to dashboard Cite

A novel medical image quality index using grey relational coefficient calculation is proposed in this study. Three medical modalities, DR, CT and MRI, using 30 or 60 images with a total of 120 images used for experimentation. These images were first compressed at ten different compression ratios (10 ∼ 100) using a medical image compression algorithm named JJ2000. Following that, the quality of the reconstructed images was evaluated using the grey relational coefficient calculation. The results were shown consistent with popular objective quality metrics. The impact of different image aspects on four grey relational coefficient methods were further tested. The results showed that these grey relational coefficients have different slopes but very high consistency for various image areas. Nagai's grey relational coefficient was chosen in this study because of higher calculation speed and sensitivity. A comparison was also made between this method and other windows-based objective metrics for various window sizes. Studies found that the grey relational coefficient results are less sensitive to window size changes. The performance of this index is better than some windows-based objective metrics and can be used as an image quality index.

show abstract

Wavelet-Based CR Image Denoising by Exploiting Inner-Scale Dependency

Chen

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A statistical method for evaluation quality of medical images: a case study in bit discarding and image compression

Cited by 16 publications

References 18 publications

A Blurring Index for Medical Images

A Blurring Index for Medical Images

A Novel Medical Image Quality Index

Wavelet-Based CR Image Denoising by Exploiting Inner-Scale Dependency

Contact Info

Product

Resources

About