14 research outputs found

    Adaptive Noise Reduction of Scintigrams with a Wavelet Transform

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    The aim of this study was to eliminate the effect of Poisson noise in scintigrams with a wavelet thresholding method. We developed a new noise reduction method with a wavelet transform. The proposed method was a combination of the translation-invariant denoising method and our newly introduced denoising filter which was applicable for Poisson noise. To evaluate the validity of our proposed method, phantom images and scintigrams were used. The results with the phantom images showed that our method was better than conventional methods in terms of the peak signal-to-noise ratio by 3 dB. Quality of the scintigrams processed with our method was better than that with the conventional methods in terms of reducing Poisson noise while preserving edge components. The results demonstrated that the proposed method was effective for the reduction of Poisson noise in scintigrams

    BL_Wiener Denoising Method for Removal of Speckle Noise in Ultrasound Image

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    Medical imaging techniques are extremely important tools in medical diagnosis. One of these important imaging techniques is ultrasound imaging. However, during ultrasound image acquisition process, the quality of image can be degraded due to corruption by speckle noise. The enhancement of ultrasound images quality from the 2D ultrasound imaging machines is expected to provide medical practitioners more reliable medical images in their patients’ diagnosis. However, developing a denoising technique which could remove noise effectively without eliminating the image’s edges and details is still an ongoing issue. The objective of this paper is to develop a new method that is capable to remove speckle noise from the ultrasound image effectively. Therefore, in this paper we proposed the utilization of Bilateral Filter and Adaptive Wiener Filter (BL_Wiener denoising method) for images corrupted by speckle noise. Bilateral Filter is a non-linear filter effective in removing noise, while Adaptive Wiener Filter balances the tradeoff between inverse filtering and noise smoothing by removing additive noise while inverting blurring. From our simulation results, it is found that the BL_Wiener method has improved between 0.89 [dB] to 3.35 [dB] in terms of PSNR for test images in different noise levels in comparison to conventional methods

    A Based Bayesian Wavelet Thresholding Method to Enhance Nuclear Imaging

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    Nuclear images are very often used to study the functionality of some organs. Unfortunately, these images have bad contrast, a weak resolution, and present fluctuations due to the radioactivity disintegration. To enhance their quality, physicians have to increase the quantity of the injected radioactive material and the acquisition time. In this paper, we propose an alternative solution. It consists in a software framework that enhances nuclear image quality and reduces statistical fluctuations. Since these images are modeled as the realization of a Poisson process, we propose a new framework that performs variance stabilizing of the Poisson process before applying an adapted Bayesian wavelet shrinkage. The proposed method has been applied on real images, and it has proved its performance

    A Based Bayesian Wavelet Thresholding Method to Enhance Nuclear Imaging

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    Nuclear images are very often used to study the functionality of some organs. Unfortunately, these images have bad contrast, a weak resolution, and present fluctuations due to the radioactivity disintegration. To enhance their quality, physicians have to increase the quantity of the injected radioactive material and the acquisition time. In this paper, we propose an alternative solution. It consists in a software framework that enhances nuclear image quality and reduces statistical fluctuations. Since these images are modeled as the realization of a Poisson process, we propose a new framework that performs variance stabilizing of the Poisson process before applying an adapted Bayesian wavelet shrinkage. The proposed method has been applied on real images, and it has proved its performance

    PLANAR SCINTIGRAPHY IMAGE DE-NOISING USING COIFLET WAVELET

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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

    Medical imaging analysis with artificial neural networks

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    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

    REGION EXTRACTION OF OPTIC NERVE HEAD IN A 3D OPTICAL COHERENCE TOMOGRAPHY

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    Optical coherence tomography (OCT) is a non-invasive technique to investigate the microstructure of biological tissue and used for diagnosing retinal diseases like glaucoma and diabetes. OCT is based on low-coherent interferometry, and so speckle noise degrades the quality of OCT images. The purpose of this study is to improve image quality and extract optic nerve head which has a close relationship with glaucoma. The proposed system uses de-noising and contrast enhancement techniques which are based on wavelet transform, and binary image processing of extraction of optic nerve head. The wavelet de-noising method could yield better contrast than the general smoothing results, and wavelet contrast enhancement sharpened image feature. The binary image processing could extract visible optic nerve head, but some parameters have been set manually in the binary process

    Restauration des images en scintigraphie planaire et SPECT suite à la réduction des doses administrées et des temps de pause

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    This work consists in decreasing the administered dose in planar imaging to reduce patient exposure and acquisition time to avoid motion artifacts. This has been addressed by developing appropriate treatment approaches for conservation or even improved diagnostic quality images. We have led to a method of restoring which proves the most appropriate for the maximum recovery of diagnostic images detail. We realized our acquisitions in vitro on phantoms that we designed to simulate the main event of variability in activity seen on clinical cases as well as on standard phantoms.Ce travail consiste à réduire la dose administrée en scintigraphie planaire afin de réduire l'exposition des patients et à réduire le temps d'acquisition pour éviter les artéfacts de mouvement. Ceci a été abordé en élaborant des approches de traitement adéquates permettant la conservation ou même l'amélioration des qualités diagnostiques des images. Nous avons aboutit à une méthode de restauration qui s'avère la plus adéquate pour la récupération de maximum de détails de diagnostics des images. Nous avons réalisé nos acquisitions in-vitro sur des fantômes que nous avons conçus de manière à simuler les principaux cas de variabilité de l'activité remarquée sur des cas cliniques ainsi que sur des fantômes standards
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