Optimization Method to Predict Optimal Noise Reduction Parameters for the Non-Local Means Algorithm Based on the Scintillator Thickness in Radiographyopen access
- Authors
- Cha, Bo Kyung; Lee, Kyeong-Hee; Lee, Youngjin; Kim, Kyuseok
- Issue Date
- Dec-2023
- Publisher
- MDPI
- Keywords
- noise reduction; parameter optimization; scintillator thickness; image restoration; non-local means (NLM) method; image quality assessment
- Citation
- SENSORS, v.23, no.24
- Journal Title
- SENSORS
- Volume
- 23
- Number
- 24
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/90264
- DOI
- 10.3390/s23249803
- ISSN
- 1424-8220
1424-3210
- Abstract
- The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 mu m), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future.
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