Shigehito Suzuki

Realization of reliable cerebral-blood-flow maps from low-dose CT perfusion images by statistical noise reduction using nonlinear diffusion filtering.

X-ray computed tomographic perfusion (CTP) imaging, a rapid method for measuring cerebral blood flow (CBF), is an effective modality for assessment of the severity and extent of brain tissue ischemia. Low-dose scanning has been required for CTP imaging for reducing the radiation exposure to patients, because the same plane is scanned repeatedly. Low-dose CTP imaging, however, results in substantial statistical noise in the images, which may negatively impact the accuracy of CBF values. Because CBF values are calculated from the set of CTP images, it is important to reduce the statistical noise in raw CTP images to make the values reliable. Noise reduction must be performed without blurring of vessel structures, because such blurring will overestimate CBF values. For this purpose, two-dimensional nonlinear diffusion filtering (NLDF) was introduced. It was applied to CTP images of a CTP phantom for evaluating the accuracy of CBF values in low-dose CTP and to clinical low-dose CTP images for determining its effectiveness in actual CTP examinations. NLDF successfully reduced the statistical noise in the CTP images while preserving the sharp edges. This feature generated CBF values close to the reference value, producing reliable CBF maps from low-dose CT perfusion images. The CBF maps obtained with NLDF were comparable to or better than those obtained by other, commercial CTP software programs. The use of NLDF was thus effective for manipulation of low-dose CT perfusion images.

Spike noise removal in the scanning laser microscopic image of diamond abrasive grain using a wavelet transform

To remove spike noise in the scanning laser microscopic image of diamond abrasive grain without blurring the sharp edges, a new smoothing technique that combines a conventional averaging technique with wavelet transforms is proposed. The diamond abrasive grain image is decomposed into high- and low-frequency subimages using wavelet filters, and all subimages except the lowest frequency one are synthesized to obtain a high-frequency image, from whose pixel values spike noise points are extracted. A conventional averaging technique is then applied to the same points in the original image as the spike noise points in the high-frequency image. The smoothing technique successfully removes both clustered and unclustered spike noise while preserving the sharp edges. Spike noise is removed without a loss in the original grain shape. This smoothing technique will surely be effective for other applications.

Novel denoising technique based on filtering the coefficients of a redundant wavelet transform

A denoising technique based on filtering the coefficients of a redundant wavelet transform is proposed for the purpose of reducing statistical noise in images without blurring sharp edges. The wavelet coefficients in the higher-spatial-frequency components are denoised using a function that has been used in nonlinear diffusion filtering. With this function, edge and noise points were correctly distinguished. This resulted in the elimination of the wavelet coefficients at the noise points and the preservation of the wavelet coefficients at the edge points. As a result, most of the image noise was successfully eliminated without appreciable losses in the sharp edges. Thus, the denoising technique coped with both noise reduction and edge preservation.

Wavelet-based pre-smoothing and post-denoising for iterative image reconstruction for emission tomography

A wavelet-based smoothing technique based on hard and soft thresholding is proposed to eliminate statistical noise interference in the reconstruction of emission computed tomography. The interference is due to Poisson noise in the projection data. Pre-smoothing for projection data is performed to reduce the noise interference, together with post-smoothing for reconstructed images from the pre-smoothed projection data. The present work also investigates not only optimal parameter values for hard and soft thresholding but also the reason why the parameter values produced good smoothing results. An analysis was made about parameter values for thresholding with which good smoothing results were produced for the purpose of explaining the reason. Based on this analysis, an ad-hoc denoising algorithm is proposed for pre- and post- smoothing.

Image Smoothing Based on Fuzzy Reasoning. I. Theory.

画像平滑化において相反する要素である統計的雑音除去とエッジ保存の双方を同時に満足させるため, ファジィ推論を平滑化に利用した。画素のエッジ情報から, その最適な平滑化の方法を言語的規則を用いて推論し, その画素の平滑値を決定した。この平滑化の方法は, 言語的規則を用いる点で, 数理解析的規則を使用する従来の平滑化の方法と根本的に異なる。