Takahide Okamoto

Noise reduction using wavelet with application to medical X-ray image

When the signal is embedded in an additive Gaussian noise, its estimation is often done by finding a wavelet basis that concentrates the signal energy over few coefficients and by thresholding the noisy coefficients. However, in many practical problems such as medical X-ray image, astronomical and low-light image, the recorded data are not modeled by Gaussian noise but as the realization of a Possion process. In this paper, we propose a new approach to remove Poisson noise from medical X-ray image in the wavelet domain. This method improves the conventional BayesShrink approach based on wavelet coefficients characteristics of medical X-ray image. In order to remove the large-amplitude noise which cannot be removed by conventional wavelet shrink methods, we propose a new type of directional adaptive median filter (DAMF). The proposed method shows more excellent results in amount of simulations of image denoising than the conventional methods

Removing noise from radiological image using multineural network filter

In this paper, a new type of multineural networks filter (MNNF) is presented that is trained for restoration and enhancement of the digital radiological images. In medical radiographics, noise has been categorized as quantum mottle, which is related to the incident X-ray exposure and artificial noise, which is caused by the grid etc. MNNF consists of several neural network filters (NNFs). A novel analysis method is proposed for making clear the characteristics of the trained MNNF. In the proposed method, a characteristics judgement system is presented to decide which NNF is executed through the standard deviation value of input region. The new approach is tested on 9 clinical medical X-ray images and 5 synthesized noisy X-ray images. In all cases, the proposed MNNF produces better results in terms of peak signal to noise ratio (PSNR), mean-to-standard-deviation ratio (MSR) and contrast to noise ratio (CNR) measures than the original NNF, linear inverse filter and nonlinear median filter

Denoising Projection Data with a Robust Adaptive Bilateral Filter in Low-Count SPECT

Low-count SPECT images are well known to be smoothed strongly by a Butterworth filter for statistical noise reduction. Reconstructed images have a low signal-to-noise ratio (SNR) and spatial resolution because of the removal of high-frequency signal components. Using the developed robust adaptive bilateral filter (RABF), which was designed as a pre-stage filter of the Butterworth filter, this study was conducted to improve SNR without degrading the spatial resolution for low-count SPECT imaging. The filter can remove noise while preserving spatial resolution. To evaluate the proposed method, we extracted SNR and spatial resolution in a phantom study. We also conducted paired comparison for visual image quality evaluation in a clinical study. Results show that SNR was increased 1.4 times without degrading the spatial resolution. Visual image quality was improved significantly (p < 0.01) for clinical low-count data. Moreover, the accumulation structure became sharper. A structure embedded in noise emerged. Our method, which denoises without degrading the spatial resolution for low-count SPECT images, is expected to increase the effectiveness of diagnosis for low-dose scanning and short acquisition time scanning.

Adapted erase method using ultraviolet light and the influence of ghosting image on a clinical CR image

In Storage Phosphor (SP) used for Computed Radiography (CR), the quite stable latent image remains due to impurities and the lattice imperfections by the existence of trapped electron and hole. The quite stable latent image appears again (Ghosting image) by the passage of time etc, is recognized as image, and becomes an artifact in a clinical CR image. This study verified the influence of Ghosting image on a clinical image by a physical characteristic and the subjective evaluation, and examined the method to delete this artifact by the exposure of ultraviolet light as a method of improving image. As a result, Ghosting image can be confirmed by the dose used by the clinical diagnosis study, and it is taken as deterioration of the granularity on a physical characteristic. The decrease of the granularity of about 15% (by winner spectrum) was admitted by the frequency band of 2cycle/mm in SP that had been used for a long term. As the method of improving these, Ghosting image was erased with the ultraviolet light lamp with the peak wavelength at 310nm, and has band from 290 nm to 320 nm, and is useful for the improvement of the image quality. In this study, we examine the influence of Ghosting image on a clinical image, and report on the method to delete them by the exposure to ultraviolet light radiation for the image quality improvement plan that uses the x-ray used for usual clinical diagnosis study.

Noise reduction in digital radiography by wavelet packet using nonlinear correlation threshold

The influence of quantum mottle appears as degradation of graininess with reduction of the amount of incidence X-rays. The results of a Wiener spectrum study showed that graininess increased as the dose was reduced, and noise affected all frequencies. However, in clinical images, these effects are seen only in the high-frequency domain above 0.3 cycle/mm. Moreover, the effects of a grid are restricted to a parallel component or a perpendicular component based on its structure. And the influence appears in the decomposition wavelet image of H or V. From these result, the decomposed wavelet coefficients at complete binary tree are divided into seven frequency-coefficient bands. About the noise processing method, we tried to reduce noise by applying modification of Wavelet Transform Modules Maxima method proposed by Mallet, et al. Then we tried to the adaptive nonlinear threshold based on wiener spectrum study and power spectrum study. Based on the above considerations, evaluation was performed using clinical radiographs obtained at a standard dose and reduced dose with the noise reduction processing applied. The results showed that noise caused by quantum mottle and the grid can be reduced by this method without the need for threshold processing based on clinical experience.