Body Fat Assessment Method Using CT Images with Separation Mask Algorithm

In recent years, the number of obese population in Korea has been growing up along with the economic development, environmental factors, and the change in life style. Considering the growth of obese population and the adverse effect of obesity on health, it is getting more important to prevent and diagnose the obesity with the quantitative measurement of body fat that has become an important indicator for obesity. In this study, we proposed a procedure for the automated fat assessment from computed tomography (CT) data using image processing technique. The proposed method was applied to a single-CT image as well as CT-volume data, and results were correlated to those of dual-energy X-ray absorptiometry (DEXA) that is known as the reliable method for evaluating body fat. Using single-CT images, correlation coefficients between DEXA and the automated assessment and DEXA and the manual assessment were 0.038 and 0.058, respectively (P > 0.05). Hence, there was no significant correlation between three methods using the proposed method with single-CT images. On the other hand, in case of CT-volume data, the above correlation coefficients were increased to 0.826, 0.812, and 0.805, respectively (P < 0.01). Thus, DEXA and the proposed methods with CT-volume data showed highly significant correlation with each other. The results suggest that the proposed automated assessment using CT-volume data is a reliable method for the evaluation of body fat. It is expected that the clinical application of the proposed procedure will be helpful to reduce the time for the quantitative evaluation of patient’s body fat.     

Novel Genetic-Neuro-Fuzzy Filter for Speckle Reduction from Sonography Images

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations.The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filters output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.     

Bayesian Framework-Based Adaptive Hybrid Filtering for Speckle Noise Reduction in Ultrasound Images Via Lion Plus FireFly Algorithm

Journal of Digital Imaging - The existence of speckle noise in ultrasound (US) image processing distorts the image quality and also hinders the development of systematic approaches for US images....     

Evaluation of Apparent Noise on CT Images Using Moving Average Filters

This study aims to devise a simple method for evaluating the magnitude of texture noise (apparent noise) observed on computed tomography (CT) images scanned at a low radiation dose and reconstructed using iterative reconstruction (IR) and deep learning reconstruction (DLR) algorithms, and to evaluate the apparent noise in CT images reconstructed using the filtered back projection (FBP), IR, and two types of DLR (AiCE Body and AiCE Body Sharp) algorithms. We set a square region of interest (ROI) on CT images of standard- and obese-sized low-contrast phantoms, slid different-sized moving average filters in the ROI vertically and horizontally in steps of 1 pixel, and calculated the standard deviation (SD) of the mean CT values for each filter size. The SD of the mean CT values was fitted with a curve inversely proportional to the filter size, and an apparent noise index was determined from the curve-fitting formula. The apparent noise index of AiCE Body Sharp images for a given mAs value was approximately 58, 23, and 18% lower than that of the FBP, AIDR 3D, and AiCE Body images, respectively. The apparent noise index was considered to reflect noise power spectrum values at lower spatial frequency. Moreover, the apparent noise index was inversely proportional to the square roots of the mAs values. Thus, the apparent noise index could be a useful indicator to quantify and compare texture noise on CT images obtained with different scan parameters and reconstruction algorithms.

     

Evaluation of a Noise Reduction Procedure for Chest Radiography

Background

The aim of this study was to evaluate the usefulness of noise reduction procedure (NRP), a function in the new image processing for chest radiography.

Methods

A CXDI-50G Portable Digital Radiography System (Canon) was used for X-ray detection. Image noise was analyzed with a noise power spectrum (NPS) and a burger phantom was used for evaluation of density resolution. The usefulness of NRP was evaluated by chest phantom images and clinical chest radiography. We employed the Bureau of Radiological Health Method for scoring chest images while carrying out our observations.

Results

NPS through the use of NRP was improved compared with conventional image processing (CIP). The results in image quality showed high-density resolution through the use of NRP, so that chest radiography examination can be performed with a low dose of radiation. Scores were significantly higher than for CIP.

Conclusion

In this study, use of NRP led to a high evaluation in these so we are able to confirm the usefulness of NRP for clinical chest radiography.     

Automated Pediatric Abdominal Effective Diameter Measurements Versus Age-Predicted Body Size for Normalization of CT Dose

There has been increasing interest in adjusting CT radiation dose data for patient body size. A method for automated computation of the abdominal effective diameter of a patient from a CT image has previously only been tested in adult patients. In this work, we tested the method on a set of 128 pediatric patients aged 0.8 to 12.9 years (average 8.0 years, SD?=?3.7 years) who had CT abdomen/pelvis exams performed on a Toshiba Aquilion 64 scanner. For this set of patients, age-predicted abdominal effective diameter extrapolated based on data from the International Commission on Radiation Units and Measurements was a relatively poor predictor of measured effective diameter. The mean absolute percentage error between the CTDI normalization coefficient calculated from a manually measured effective diameter and the coefficient determined by age-predicted effective diameter was 12.3 % with respect to a 32 cm phantom (range 0.0–52.8 %, SD 8.7 %) and 12.9 % with respect to a 16 cm phantom (range 0.0–56.4 %, SD 9.2 %). In contrast, there is a close correspondence between the automated and manually measured patient effective diameters, with a mean absolute error of 0.6 cm (error range 0.2–1.3 cm). This correspondence translates into a high degree of correspondence between normalization coefficients determined by automated and manual measurements; the mean absolute percentage error was 2.1 % with respect to a 32 cm phantom (range 0.0–8.1 %, SD?=?1.4 %) and 2.3 % with respect to a 16 cm phantom (range 0.0–9.3 %, SD?=?1.6 %).     

A Hybrid Algorithm to Enhance Colour Retinal Fundus Images Using a Wiener Filter and CLAHE

Journal of Digital Imaging - Digital images used in the field of ophthalmology are among the most important methods for automatic detection of certain eye diseases. These processes include image...     

基于自适应方向性滤波和非局部均f值修补的CT图像金属伪影消除

CT图像金属伪影的校正长期以来都是一个重要课题.首先采用自适应方向性前置滤波器对含有金属伪影的图像进行处理,在一定程度上消除噪声,抑制条状伪影;其后采用均值漂移分割出原图像中的金属成分,最大互信息熵分割出伪影成分;使用平行束投影获取原始图像和金属成分的弦空间数据,从原图弦空间数据中减除伪影成分对应的弦图;继而采用非局部均值修补结合线性插值方法对弦空间数据进行补全;最后采样滤波反投影得到校正后的图像.实验表明,本算法对于含有金属伪影的水模和真实临床图像的校正,获得更好的匀质区域一致性和更好的伪影抑制性能.     

Development of a Voxel-Matching Technique for Substantial Reduction of Subtraction Artifacts in Temporal Subtraction Images Obtained from Thoracic MDCT

A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as formation of new lesions and changes in existing abnormalities) on medical images by removing most of the normal structures. However, subtraction artifacts are commonly included in temporal subtraction images obtained from thoracic computed tomography and thus tend to reduce its effectiveness in the detection of pulmonary nodules. In this study, we developed a new method for substantially removing the artifacts on temporal subtraction images of lungs obtained from multiple-detector computed tomography (MDCT) by using a voxel-matching technique. Our new method was examined on 20 clinical cases with MDCT images. With this technique, the voxel value in a warped (or nonwarped) previous image is replaced by a voxel value within a kernel, such as a small cube centered at a given location, which would be closest (identical or nearly equal) to the voxel value in the corresponding location in the current image. With the voxel-matching technique, the correspondence not only between the structures but also between the voxel values in the current and the previous images is determined. To evaluate the usefulness of the voxel-matching technique for removal of subtraction artifacts, the magnitude of artifacts remaining in the temporal subtraction images was examined by use of the full width at half maximum and the sum of a histogram of voxel values, which may indicate the average contrast and the total amount, respectively, of subtraction artifacts. With our new method, subtraction artifacts due to normal structures such as blood vessels were substantially removed on temporal subtraction images. This computerized method can enhance lung nodules on chest MDCT images without disturbing misregistration artifacts.     

Properties of Noise in Positron Emission Tomography Images Reconstructed with Filtered-Backprojection and Row-Action Maximum Likelihood Algorithm

Noise levels observed in positron emission tomography (PET) images complicate their geometric interpretation. Post-processing techniques aimed at noise reduction may be employed to overcome this problem. The detailed characteristics of the noise affecting PET images are, however, often not well known. Typically, it is assumed that overall the noise may be characterized as Gaussian. Other PET-imaging-related studies have been specifically aimed at the reduction of noise represented by a Poisson or mixed Poisson + Gaussian model. The effectiveness of any approach to noise reduction greatly depends on a proper quantification of the characteristics of the noise present. This work examines the statistical properties of noise in PET images acquired with a GEMINI PET/CT scanner. Noise measurements have been performed with a cylindrical phantom injected with ¹¹C and well mixed to provide a uniform activity distribution. Images were acquired using standard clinical protocols and reconstructed with filtered-backprojection (FBP) and row-action maximum likelihood algorithm (RAMLA). Statistical properties of the acquired data were evaluated and compared to five noise models (Poisson, normal, negative binomial, log-normal, and gamma). Histograms of the experimental data were used to calculate cumulative distribution functions and produce maximum likelihood estimates for the parameters of the model distributions. Results obtained confirm the poor representation of both RAMLA- and FBP-reconstructed PET data by the Poisson distribution. We demonstrate that the noise in RAMLA-reconstructed PET images is very well characterized by gamma distribution followed closely by normal distribution, while FBP produces comparable conformity with both normal and gamma statistics.     

一种基于纹理的颅脑CT图像病变检出算法

以颅脑CT图像为研究对象用基于纹理的数字化统计图谱方法进行了病变自动化检出的研究,提出并创建基于纹理的数字化统计图谱——纹理层析图谱。通过比较待诊断颅脑CT图像与纹理层析图谱间的差异,实现颅脑CT图像中多种病变的计算机自动化检出。实验结果表明,在不知道病变种类的前提下,基于纹理层析图谱的病变检出算法可以实现颅脑CT图像所含病变的自动化检出。利用图像的纹理信息变化是实现颅脑CT图像病变检出的一个有效途径。     

搜索空间逐步缩小的遗传算法用于尿沉渣图像特征选择的研究

基于尿沉渣图像特征选择问题,提出一种新的特征优选方法,首先引入类空间分层分类思想,将多类成分特征集优选问题转化为两类成分的特征集优选问题以减少特征数,从而减少了后续分类器的维数复杂度并提高了优选后的特征集对成分的识别率;针对红细胞和白细胞的特征集优选问题,采用改进的遗传算法进行处理,先根据统计实验结果,锁定待选特征集中形态特征和纹理特征相应的两个可分度最大且相互独立的特征,然后使用基因位逐步锁定技术,结合小生境技术和自适应交叉变异算子,提高了遗传算法的搜索性能;最后,为了提高特征集的优选效果和稳定性,引入"多票投选"机制。就多个尿沉渣成分样本进行验证实验,结果表明,该算法优选的特征集与通过其他方式获得的特征集相比,识别率较高,而且明显减少了后续分类器的维数复杂度。     

A Motion Compounding Technique for Speckle Reduction in Ultrasound Images

The quality of ultrasound images is usually influenced by speckle noise and the temporal decorrelation of the speckle patterns. To reduce the speckle noise, compounding techniques have been widely applied. Partially correlated images scanned on the same subject cross-section are combined to generate a compound image with improved image quality. However, the compounding technique might introduce image blurring if the transducer or the target moves too fast. This blurring effect becomes especially critical when assessing tissue deformation in clinical motion examinations. In this paper, an ultrasound motion compounding system is proposed to improve the quality of ultrasound motion sequences. The proposed motion compounding technique uses a hierarchical adaptive feature weighted motion estimation method to realign the frames before compounding. Each frame is first registered and warped to the reference frame before being compounded to reduce the speckle noise. Experimental results showed that the motion could be assessed accurately and better visualization could be achieved for the compound images, with improved signal-to-noise and contrast-to-noise ratios.     

An Efficient Pipeline for Abdomen Segmentation in CT Images

Journal of Digital Imaging - Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation....     

诱发电位提取的聚类分析和小波去噪复合方法

本文研究了提取诱发电位的一种复合方法,它用聚类方法对诱发电位信号进行筛选,将筛选出的信号进行叠加以消除测试数据不一致性对叠加结果的影响.然后利用小波方法进一步去除噪声,提高信噪比.文中介绍了模糊聚类方法和小波去噪理论,并通过仿真计算来评估提取效果.仿真计算表明该方法可减少测试次数,提高信噪比,对视觉脑干诱发电位处理结果显示,该方法的实际处理效果良好.     

牙颌CT图像序列中牙的半自动分割方法

牙颌CT图像序列相邻切片之间,相应牙的大小、位置以及牙区域和轮廓的灰度分布等特征比较接近,并呈一定的变化规律,根据这一特点提出了牙颌CT图像序列中牙的半自动分割方法。首先选取参考切片,加入少量用户操作进行参考切片中牙轮廓的提取,接着以参考切片为起始切片,由已完成轮廓提取的牙包围盒作为待处理切片(相邻切片)相应牙的操作区间,然后在此区间内用区域生长法提取牙轮廓,由此逐张切片处理可以自动地得到所有切片全牙列每颗牙的轮廓。实验结果表明,本方法仅需少量用户交互就能快速、基本准确地从牙颌CT图像序列中分割出牙轮廓,具有一定的实用价值。     

Development of Personalized Annuloplasty Rings: Combination of CT Images and CAD-CAM Tools

Although the use of personalized annuloplasty rings manufactured for each patient according to the size and morphology of their valve complex could be beneficial for the treatment of mitral insufficiency, this possibility has been limited for reasons of timelines and costs as well as for design and manufacturing difficulties, as has been the case with other personalized implant and prosthetic developments. However, the present quality of medical image capture equipment together with the benefits to be had from computer-aided design and manufacturing technologies (CAD-CAM) and the capabilities furnished by rapid prototyping technologies, present new opportunities for a personalized response to the development of implants and prostheses, the social impact of which could turn out to be highly positive. This paper sets out a personalized development of an annuloplasty ring based on the combined use of information from medical imaging, from CAD-CAM design programs and prototype manufacture using rapid prototyping technologies.     

基于多分辨率GMI Demons算法的18F-FDG PET-CT图像配准在食道癌病例中的应用

18F-FDG PET和CT图像的精确配准在肿瘤的放射治疗中具有重要的临床研究意义,本研究采用全局刚性粗配准对食道癌病例中的PET和CT图像进行预处理,尽可能地减小摆位误差,然后使用基于互信息梯度的Demons算法(GMI Demons)进行局部形变配准,有效弥补内部器官误差,另外为了加快配准过程,保持图像的鲁棒性的同时避免局部极值,在形变配准前使用多分辨率图像金字塔结构。通过对10例食道癌病例的定量分析,最大互信息值结果说明经GMI Demons算法配准之后的图像精度比基于MI算法要提高8.046%±0.041%,配准前后临床上肿瘤靶区(GTV)大小的变化,说明经GMI Demons算法配准之后的GTV大小比基于MI算法配准之后的精度提高8.022%±0.044%。两种定量结果的一致性和通过对图像的定性分析,说明该配准策略可以快速地精确肿瘤靶区位置,在制定精确的放疗计划和实际的临床应用中具有研究意义。     

Sustained Reduction and Prevention of Neonatal and Pediatric Central Line-Associated Bloodstream Infection Following a Nurse-Driven Quality Improvement Initiative in a Pediatric Facility

Purpose

Hospitals devote significant resources developing protocols to minimize the incidence of central line-associated bloodstream infections (CLABSIs), a source of increased patient morbidity and health care costs; however, few of these protocols, especially centralized protocols, are reported in the literature. This study characterizes the development and effectiveness of a pediatric hospital's centralized CLABSI prevention bundle.