Time series analysis of covariance based on linear transfer function models

Azimmohseni, Majid; Khalafi, Mahnaz; Kordkatuli, M.

doi:10.1007/s11203-018-9182-z

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Transfer function usage depends on the image type, and which imprints information or features that need to be emphasized or displayed. The most common types are the linear [24], logarithmic [25], gamma [26] and inverse linear (negative) transfer functions [27]. Transfer functions can help increase the contrast, and in turn, use more of the available range of brightness values.…”

Section: Transfer Function Techniquementioning

confidence: 99%

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Okuwobi

Ding

Wan

et al. 2022

Complex Intell. Syst.

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Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

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Section: Transfer Function Techniquementioning

confidence: 99%

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Okuwobi

Ding

Wan

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Intervention Time Series Analysis and Forecasting of Organ Donor Transplants in the US during the COVID-19 Era

Malladi

2023

Forecasting

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The COVID-19 pandemic has had a catastrophic effect on the healthcare system including organ transplants worldwide. The number of living donor transplants performed in the US was affected more significantly by the pandemic with a 22.6% decrease in counts from 2019 to 2020 due to concerns of unnecessarily exposing potential living donors and living donor recipients to possible COVID-19 infection. This paper examines donor transplant counts obtained from the United Network for Organ Sharing from January 2002 to August 2021 using an intervention time series model with March 2020 as the intervention event. Specifically, donor transplant counts are analyzed across the different organs, donor types, and some major individual sociocultural factors, which are potential conditions contributing to disparities in achieving donor transplant equity such as age, ethnicity, and gender. In addition, the kidney allocation policy implemented in March 2021 is introduced as a second intervention event for kidney donor transplants. Overall, forecasts generated by our methods are more accurate than those using seasonal autoregressive integrated moving average models without interventions and seasonal naive methods. The intervention time series model provides a forecast accuracy comparable to the exponential smoothing method.

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Time series analysis of covariance based on linear transfer function models

Cited by 2 publications

References 21 publications

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Intervention Time Series Analysis and Forecasting of Organ Donor Transplants in the US during the COVID-19 Era

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