Search

Existing integer-order Nonlinear Anisotropic Diffusion (NAD) used in noise suppressing will produce undesirable staircase effect or speckle effect. In this paper, we propose a new scheme, named Fractal-order Perona-Malik Diffusion (FPMD), which replaces the integer-order derivative of the Perona-Malik (PM) Diffusion with the fractional-order derivative using G-L fractional derivative. FPMD, which is a interpolation between integer-order Nonlinear Anisotropic Diffusion (NAD) and fourth-order partial differential equations, provides a more flexible way to balance the noise reducing and anatomical details preserving. Smoothing results for phantoms and real sinograms show that FPMD with suitable parameters can suppress the staircase effects and speckle effects efficiently. In addition, FPMD also has a good performance in visual quality and root mean square errors (RMSE). [ABSTRACT FROM AUTHOR]

Existing sinogram restoration methods cannot handle noises and nonstationary artifacts simultaneously. Although bilateral filter provides an efficient way to preserve image details while denoising, its performance in sinogram restoration for low-dosed X-ray computed tomography (LDCT) is unsatisfied. The main reason for this situation is that the range filter of the bilateral filter measures similarity by sinogram values, which are polluted seriously by noises and nonstationary artifacts of LDCT. In this paper, we propose a simple method to obtain satisfied restoration results for sinogram of LDCT. That is, the range filter weighs the similarity by Gaussian smoothed sinogram. Since smoothed sinogram can reduce the influence of both noises and nonstationary artifacts for similarity measurement greatly, our new method can provide more satisfied denoising results for sinogram restoration of LDCT. Experimental results show that our method has good visual quality and can preserve anatomy details in sinogram restoration even in both noises and non- stationary artifacts. [ABSTRACT FROM AUTHOR]

Purpose: In the segmentation of sequential treatment-time CT prostate images acquired in image-guided radiotherapy, accurately capturing the intrapatient variation of the patient under therapy is more important than capturing interpatient variation. However, using the traditional deformable-model-based segmentation methods, it is difficult to capture intrapatient variation when the number of samples from the same patient is limited. This article presents a new deformable model, designed specifically for segmenting sequential CT images of the prostate, which leverages both population and patient-specific statistics to accurately capture the intrapatient variation of the patient under therapy. Methods: The novelty of the proposed method is twofold: First, a weighted combination of gradient and probability distribution function (PDF) features is used to build the appearance model to guide model deformation. The strengths of each feature type are emphasized by dynamically adjusting the weight between the profile-based gradient features and the local-region-based PDF features during the optimization process. An additional novel aspect of the gradient-based features is that, to alleviate the effect of feature inconsistency in the regions of gas and bone adjacent to the prostate, the optimal profile length at each landmark is calculated by statistically investigating the intensity profile in the training set. The resulting gradient-PDF combined feature produces more accurate and robust segmentations than general gradient features. Second, an online learning mechanism is used to build shape and appearance statistics for accurately capturing intrapatient variation. Results: The performance of the proposed method was evaluated on 306 images of the 24 patients. Compared to traditional gradient features, the proposed gradient-PDF combination features brought 5.2% increment in the success ratio of segmentation (from 94.1% to 99.3%). To evaluate the effectiveness of online learning mechanism, the authors carried out a comparison between partial online update strategy and full online update strategy. Using the full online update strategy, the mean DSC was improved from 86.6% to 89.3% with 2.8% gain. On the basis of full online update strategy, the manual modification before online update strategy was introduced and tested, the best performance was obtained; here, the mean DSC and the mean ASD achieved 92.4% and 1.47 mm, respectively. Conclusions: The proposed prostate segmentation method provided accurate and robust segmentation results for CT images even under the situation where the samples of patient under radiotherapy were limited. A conclusion that the proposed method is suitable for clinical application can be drawn. [ABSTRACT FROM AUTHOR]

The x-ray exposure to patients has become a major concern in computed tomography (CT) and minimizing the radiation exposure has been one of the major efforts in the CT field. Due to plenty high-attenuation tissues in the human chest, under low-dose scan protocols, thoracic low-dose CT (LDCT) images tend to be severely degraded by excessive mottled noise and non-stationary streak artifacts. Their removal is rather a challenging task because the streak artifacts with directional prominence are often hard to discriminate from the attenuation information of normal tissues. This paper describes a two-step processing scheme called 'artifact suppressed large-scale nonlocal means' for suppressing both noise and artifacts in thoracic LDCT images. Specific scale and direction properties were exploited to discriminate the noise and artifacts from image structures. Parallel implementation has been introduced to speed up the whole processing by more than 100 times. Phantom and patient CT images were both acquired for evaluation purpose. Comparative qualitative and quantitative analyses were both performed that allows conclusion on the efficacy of our method in improving thoracic LDCT data. [ABSTRACT FROM AUTHOR]

We present a new method to estimate noise for a single-slice sinogram of low- dose CT based on the homogenous patches centered at a special pixel, called center point, which has the smallest variance among all sinogram pixels. The homogenous patch, composed by homogenous points, is formed by the points similar to the center point using similarity sorting, similarity decreasing searching, and variance analysis in a very large neighborhood (VLN) to avoid manual selection of parameter for similarity measures.Homogenous pixels in the VLN allow us find the largest number of samples, who have the highest similarities to the center point, for noise estimation, and the noise level can be estimated according to unbiased estimation. Experimental results show that for the simulated noisy sinograms, the method proposed in this paper can obtain satisfied noise estimation results, especially for sinograms with relatively serious noises. [ABSTRACT FROM AUTHOR]

Themost critical step in grayscale medical image retrieval systems is feature extraction. Understanding the interrelatedness between the characteristics of lesion images and corresponding imaging features is crucial for image training, as well as for features extraction. A feature-extraction algorithm is developed based on different imaging properties of lesions and on the discrepancy in density between the lesions and their surrounding normal liver tissues in triple-phase contrast-enhanced computed tomographic (CT) scans. The algorithm includes mainly two processes: (1) distance transformation, which is used to divide the lesion into distinct regions and represents the spatial structure distribution and (2) representation using bag of visual words (BoW) based on regions. The evaluation of this system based on the proposed feature extraction algorithm shows excellent retrieval results for three types of liver lesions visible on triple-phase scans CT images. The results of the proposed feature extraction algorithm show that although single-phase scans achieve the average precision of 81.9%, 80.8%, and 70.2%, dual- and triple-phase scans achieve 86.3% and 88.0%. [ABSTRACT FROM AUTHOR]

Tomographic reconstruction from noisy projections do not yield adequate results. Mathematically, this tomographic reconstruction represents an ill-posed problem due to information missing caused by the presence of noise. Maximum a posteriori (MAP) or Bayesian reconstruction methods offer possibilities to improve the image quality as compared with analytical methods in particular by introducing a prior to guide the reconstruction and regularize the noise. With an aim to achieve robust utilization of continuity/connectivity information and overcome the heuristic weight update for other nonlocal prior methods, this paper proposes a novel patch similarity based mixture (PSM) prior model for tomographic reconstruction. This prior is defined by a weighted Gaussian distance between neighborhood intensities. The weight quantifies the similarity between local neighborhoods and is computed using a maximization entropy constraint. This prior is then introduced within a joint image/weight MAP computed tomography reconstruction algorithm. Several acceleration trials including Compute Unified Device Architecture (CUDA) parallelization is applied to alleviate the intensive patch distance computation involved in the joint algorithm. The method was tested with both synthetic phantoms and clinical computed tomography data and compared in accuracy with five other reconstruction algorithms which are filtered back-projection and Bayesian-based. Reconstruction results show that the proposed reconstructions are able to produce high-quality images with ensured iteration convergence. [ABSTRACT FROM AUTHOR]