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A blurring index for medical images
Tzong-Jer Chen1, Keh-Shih Chuang, Jen-Hao Chang
1Department of Medical Imaging Technology, Shu-Zen College of Medicine and Management, Luju Shiang, Kaohsiung, 82144, Taiwan. tjchen@szmc.edu.tw
Abstract:
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.
Insights
A new method, the Moran peak ratio, quantifies medical image blurring. This technique accurately measures image degradation from noise or compression, outperforming existing metrics for better image quality assessment.
Area of Science:
- Medical Imaging
- Image Processing
- Quantitative Analysis
Background:
- Medical images suffer from noise and blurring, degrading quality.
- Denoising can cause edge blurring; lossy compression exacerbates blurring with higher ratios.
- Existing objective metrics like normalized mean square error (NMSE) lack spatial blurring information.
Purpose of the Study:
- To introduce a novel, useful index for quantifying image blurring.
- To evaluate the Moran peak ratio for measuring blurring in medical images.
- To compare the proposed method against standard deviation methods.
Main Methods:
- Utilized a previously developed method: the Moran peak ratio.
- Calculated blurring based on the ratio of the processed peak in Moran's Z histogram to the original image.
- Applied the method to assess blurriness across different image compression algorithms.
Main Results:
- The Moran peak ratio effectively quantifies the degree of image blurring.
- The method demonstrates superior performance compared to the standard gray level distribution deviation.
- The technique successfully discerns blurriness introduced by various image compression methods.
Conclusions:
- The Moran peak ratio provides a valuable tool for objective, spatial measurement of image blurring.
- This method enhances the assessment of medical image quality affected by processing or compression.
- The Moran peak ratio offers a more accurate and informative approach to blur detection.