• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1527-1532
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• 2023

The noise reduction algorithm using the non-local means (NLM) approach is very efficient in nuclear medicine imaging. In this study, the applicability of the NLM noise reduction algorithm in single-photon emission computed tomography (SPECT) images with a brain phantom and the optimization of the NLM algorithm by changing the smoothing factors according to various reconstruction methods are investigated. Brain phantom images were reconstructed using filtered back projection (FBP) and ordered subset expectation maximization (OSEM). The smoothing factor of the NLM noise reduction algorithm determined the optimal coefficient of variation (COV) and contrast-to-noise ratio (CNR) results at a value of 0.020 in the FBP and OSEM reconstruction methods. We confirmed that the FBP- and OSEM-based SPECT images using the algorithm applied with the optimal smoothing factor improved the COV and CNR by 66.94% and 8.00% on average, respectively, compared to those of the original image. In conclusion, an optimized smoothing factor was derived from the NLM approach-based algorithm in brain SPECT images and may be applicable to various nuclear medicine imaging techniques in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.99-106
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• 2011

The emerging of ubiquitous computing and healthcare technologies provides us a strong platform to build sustainable healthcare applications especially those that require real-time information related to personal healthcare regardless of place. We realize that system stability, reliability and data protection are also important requirements for u-healthcare services. Therefore, in this paper, we designed a u-healthcare system which can be attached to the patient's body to measure vital signals, enhanced with USN secure sensor module. Our proposed u-healthcare system is using wireless sensor modules embedded with NLM-128 algorithm. In addition, PS-LFSR technique is applied to the NLM-128 algorithm to enable faster and more efficient computation. We included some performance statistical results in term of CPU cycles spent on NLM-128 algorithm with and without the PS-LFSR optimization for performance evaluation.