Principles and Applications of Nuclear Medical Imaging: A Survey on Recent Developments

Kharfi, Fayçal

doi:10.5772/54884

Cited by 9 publications

(6 citation statements)

References 23 publications

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“…The calculation of the value of MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) was used to test image quality after noise reduction was carried out. The calculation of the MSE and PSNR values uses equations ( 1) and (2).…”

Section: Resultsmentioning

confidence: 99%

“…In nuclear medicine, the internal radiation source is used to generate emission events. Nuclear medicine utilizes an Anger scintillation camera or gamma camera [2,3]. The types of the gamma camera, such as planar and SPECT (Single Photon Emission Computed Tomography), have an essential difference, that is planar type will only be capable of displaying a 2D image, while SPECT (Single Photon Emission Computed Tomography) will generate 3D image [4], [5].…”

Section: Introductionmentioning

confidence: 99%

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Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Puspitasari

Ningsih

Ridwan

et al. 2021

gnd

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The planar scintigraphic image usually has poor resolution and contains noise. This noise can be removed using the coiflet wavelet method so that the image quality gets better. This coiflet wavelet method is a noise reduction method based on frequency analysis. The planar scintigraphy image is the reconstructed image of the gamma radiation count data (phantom with the Cs-137 source in it). The original image is 15×15 pixel. Before the de-noising process, the image went through an interpolation process, which is to increase the pixel size of the image. The original image enlarged to 70×70, 480×480, and 1200×1200 pixel. After de-noising with coiflet wavelet, the image quality is measured based on MSE and PSNR parameters. The resulting images are quite good, with MSE values are close to zero and PSNR values of more than 60 dB. The smaller the MSE and the bigger the PSNR, is getting the better the image quality. In this study, the results show that the 1200×1200 pixel image has the best quality. It means that the image enlargement process has a good effect on the de-noising process, especially if the original image has a low resolution.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Puspitasari

Ningsih

Ridwan

et al. 2021

gnd

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“…They are based on the injection of a radioactive compound called a tracer into the bloodstream of the patient. The tracer sends out positrons that interact with electrons in the body [51]. The product of this interaction is used to generate a 3D image that shows the distribution of the tracer in different areas [50, 51].…”

Section: Pet/spect Imaging Of the Dopaminergic System In Pdmentioning

confidence: 99%

“…The tracer sends out positrons that interact with electrons in the body [51]. The product of this interaction is used to generate a 3D image that shows the distribution of the tracer in different areas [50, 51]. This process allows physicians and scientists to study various diseases.…”

Section: Pet/spect Imaging Of the Dopaminergic System In Pdmentioning

confidence: 99%

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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“…The collimator puts gamma rays in one direction to reach the crystal; unlike light, gamma rays cannot be concentrated using lenses [8]. Incoming gamma rays can be sensed by the collimator and the light generated by the cooperation of the gamma rays and crystal can be converted into an electronic signal by photomultiplier (PM) tubes and preamplifiers [9].…”

Section: The Collimatormentioning

confidence: 99%

Adaptive Trimmed Mean Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

Khan

Shahid

Ullah

et al. 2018

IIUMEJ

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A 2-D Adaptive Trimmed Mean Autoregressive (ATMAR) model has been proposed for denoising of medical images corrupted with poisson noise. Unfiltered images are divided into smaller chunks and ATMAR model is applied on each chunk separately. In this paper, two 5x5 windows with 40% overlapping are used to predict the center pixel value of the central row. The AR coefficients are updated by sliding both windows forward with 60% shift. The same process is repeated to scan the entire image for prediction of a new denoised image. The Adaptive Trimmed Mean Filter (ATMF) eradicates the lowest and highest variations in pixel values of the ATMAR model denoised image and also average out the remaining neighborhood pixel values. Finally, power-law transformation is applied on the resultant image of the ATMAR model for contrast stretching. Image quality is judged in terms of correlation, Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM) and Peak Signal to Noise Ratio (PSNR) of the image with latest denoising techniques. The proposed technique showed an efficient way to scale down poisson noise in scintigraphic images on a pixel-by-pixel basis.

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Principles and Applications of Nuclear Medical Imaging: A Survey on Recent Developments

Cited by 9 publications

References 23 publications

Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Planar Scintigraphy Image De-Noising Using Coiflet Wavelet

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

Adaptive Trimmed Mean Autoregressive Model for Reduction of Poisson Noise in Scintigraphic Images

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