差值法确定PET病灶生物边界及临床应用可靠性探讨

目的:探讨设计一种新的确定PET病灶边界的方法及其临床应用的可靠性。方法:自行设计开发软件,用软件打开PET图像,通过病灶中心画一条贯穿病灶的线段,两端超出病灶肉眼所见的边界少许。通过软件测出该线段上每个像素的标准化摄取值(SUV),以超出病灶两侧的两端末段线段像素上的平均SUV分别为两端各自的本底,以线段中心为起点,由病灶内向线段两侧的每个像素测得的SUV减去每侧各自的本底,差值小于或等于零处确定为病灶的边界点。通过病灶中心画数条轮辐状线段,以相同的方法确定病灶边界,将所有确定的边界点相连,即得到病灶的轮廓。选取13例确诊非小细胞肺癌病例,PET/CT显像阳性病灶15个,分别采用差值法和本底校正阈值法确定病灶边界,并计算病灶面积。结果:(1)差值法和本底校正阈值法确定15个病灶的边界不完全一致;(2)两种方法所勾画的病灶形态大致相同,测算的面积之间差异无统计学意义(P=0.609)。结论:理论上通过该方法可准确得到病灶的大小,确定病灶边界,为肿瘤放疗前确定肿瘤病灶生物边界及评价病灶疗效提供一种新的方法。     

CT图像中肺实质的自动分割

目的 为解决肺实质分割中肺部结节及高密度血管易遗漏的问题,提出一种自动肺实质分割方法.方法 首先利用二维区域生长反操作、连通区域判别等方法提取肺实质区域;然后利用行扫描法定位肺区边界点;最后通过对边界点参数分析,定位受肿瘤侵占的边界点,利用曲线拟合修复受损边界.结果 通过对多组胸部CT图像的分割,验证了算法的有效性;与几种常见边界修复算法对比,验证了行扫描边界修复算法的优越性.结论 本文提出的算法能将肿瘤包含到肺实质区域,确保分割的完整性、准确性、实时性.     

颅内上皮样血管内皮瘤1例

目的 针对目前基于胸部CT图像的肺结节自动检测方法的检出率较低且存在大量假阳性的问题,提出一种基于卷积神经网络的肺结节检测方法。方法 采用基于模糊建模思想和迭代相对模糊连接度（IRFC）算法的自动解剖识别（AAR）方法分割肺部CT图像,提取肺部实体部分;将分割后的图像输入卷积神经网络,提取肺结节特征;采用位置敏感特征图表达结节的位置信息。结果 使用天池医疗AI大赛数据集,精准分割肺部CT图像,检测肺结节的准确率、敏感度、特异度和假阳性率分别为95.60%、95.24%、95.97%和4.03%。结论 基于卷积神经网络检测肺结节有较高的精度和效率,且鲁棒性好。     

基于区域生长超声图像分割血管边界反映血管病变

背景：通过分析超声血管图像能反映血管的病变情况。目的：采用区域生长理论对超声图像进行图像分割,分析边界点的相对位移。方法：先对视频图像分帧,将动态图像转换为静态图像,采用Gabor滤波、自适应直方图量化去除超声图像噪声,然后运用区域生长法对图像做分割,接着通过开闭运算、sobel算子检测图像边界,最后提取出两条血管边界。结果与结论：通过Gabor滤波、区域生长法等手段,得到了比较好的分割结果。区域生长法在处理速度上满足了实时性要求,具有一定的通用性。并且通过分析边界点的相对位移曲线,一定程度上反映血管的病变。     

基于插值方法去除CT图像中的金属伪影

目的：对比观察线性插值、非线性插值和三次样条插值3种方法消除金属伪影后的CT图像。 方法：首先，利用全局阈值分割的方法把原图分割为金属部分和条状伪影部分。然后用雷登变换把伪影图像变换至弦空间，即得到弦图。然后用线性插值、非线性插值和三次样条插值来对伪影弦图进行处理，得到矫正的弦图。最后利用滤波反投影法重建图像。 结果：线性插值最为明显的消除了放射状条纹伪影，非线性插值在消除条纹的时候带入了噪声，而三次样条插值对伪影的处理最弱。 结论：实验表明，在各种单一插值算法中，线性插值是最为有效的消除金属伪影的插值方法。     

基于区域生长超声图像分割血管边界反映血管病变

背景:通过分析超声血管图像能反映血管的病变情况。目的:采用区域生长理论对超声图像进行图像分割,分析边界点的相对位移。方法:先对视频图像分帧,将动态图像转换为静态图像,采用Gabor滤波、自适应直方图量化去除超声图像噪声,然后运用区域生长法对图像做分割,接着通过开闭运算、sobel算子检测图像边界,最后提取出两条血管边界。结果与结论:通过Gabor滤波、区域生长法等手段,得到了比较好的分割结果。区域生长法在处理速度上满足了实时性要求,具有一定的通用性。并且通过分析边界点的相对位移曲线,一定程度上反映血管的病变。     

离散法勾画超声左心室内膜算法的进一步研究

目的 :通过离散法勾画心内膜的进一步研究 ,采用这种全自动的心内膜勾画算法 ,检验该算法对临床超声心内膜的勾画效果。方法 :改进后的离散法分为图像预处理、形心初设、形心重设和边界勾画四大步。在图像预处理中增加了直方图均衡 ,在形心初设采用中心起源的放射状边界搜索 ,在形心重设中采用了宽度不同的边界探测算子和分段阈值 ,在边界勾画中加设了边界点插入和相重点消除。改进后的离散法能够同时勾画舒张期和收缩期的心内膜边界。结果 :我们用自动勾画 5组超声图像 (其中 3组高质量图像和 2组低质量图像 )舒张期和收缩期的心内膜边界的方法来检测该算法。在 5组超声图像中我们的算法成功地勾画出 2组图像和另外 2幅舒张期图像中的心内膜 ,总体勾画成功率为 60 % ,成组勾画成功率为 40 %。结论 :由于本次勾画的难度很高 ,这样的勾画成功率已相当令人满意     

一种半自动分割脑部MRI中胼胝体的方法

针对胼胝体的图像特点以及实际应用要求,采用半自动方法对MRI中的胼胝体进行分割。首先采用基于Live-Wire的算法对胼胝体影像的起始层和终止层进行初始分割,然后利用基于距离变换的形状插值算法获取中间层的初始轮廓信息,对插值获得的初始轮廓采用Snake模型进行局部收缩,获得真实的胼胝体边界。对序列MRI脑影像中的胼胝体进行分割、重建、标定。实验结果与临床医师的使用反馈证明,本文提出的算法具有较高的灵活性与可信度,对胼胝体的分割精度与解剖统计信息相符,分割结果可满足临床需求。     

关于一个全自动勾画超声左心室内膜算法的初步研究

目的 :本文叙述一个新的全自动勾画心内膜算法的初步研究情况 ,这个算法将是全自动心功能定量分析的基础。该算法应用中心起源的形心搜索、用改进的 Sobel算子进行的放射形边界探测和依据相邻离心距差进行的边界点修正。方法 :我们用自动勾画 1 2张超声图像 (其中 6张高质量的图像 ,6张低质量的图像 )中心内膜边界的方法来检测该算法。结果 :在 1 2张超声图像中我们的算法成功地勾画出 7张图像中的心内膜 ,其中包括一张低质量的图像。结论 :虽然该算法还需要改进 ,但 58%的勾画成功率已相当令人满意     

一种改进形状插值方法在恒河猴脑黑质断层图像中的应用

背景:研究表明,脑黑质结构的病变是导致帕金森病的主要原因.从影像学角度来看,结构微小的黑质空间位置信息、体积以及3D结构形态的分析为帕金森病的临床诊断和治疗效果评价提供了十分有力的工具,因此其三维形态的研究工作尤为重要.由于恒河猴与人类的生理十分相似,这使它成为许多科学研究中比较理想的实验对象.目的:对传统的形状插值方法进行改进,并运用于恒河猴脑黑质断层图像.方法:较常用的形状插值方法是对变换后的相邻两层距离图像进行线性加权平均,从而获取中间层.试验中考虑多层相邻图像空间位置对插值图像的影响,然后进行非线性加权获取中间层.结果与结论:对插值的方法进行评估,评估结果比较令人满意,并运用于恒河猴脑黑质的三维重建中.该形状插值改进方法可以较好地运用于层片稀疏且结构细微的核团插值工作中,从而为与细微结构相关的疾病研究提供一定的参考价值.     

Real‐time Interpolation for True 3‐Dimensional Ultrasound Image Volumes

We compared trilinear interpolation to voxel nearest neighbor and distance‐weighted algorithms for fast and accurate processing of true 3‐dimensional ultrasound (3DUS) image volumes. In this study, the computational efficiency and interpolation accuracy of the 3 methods were compared on the basis of a simulated 3DUS image volume, 34 clinical 3DUS image volumes from 5 patients, and 2 experimental phantom image volumes. We show that trilinear interpolation improves interpolation accuracy over both the voxel nearest neighbor and distance‐weighted algorithms yet achieves real‐time computational performance that is comparable to the voxel nearest neighbor algrorithm (1–2 orders of magnitude faster than the distance‐weighted algorithm) as well as the fastest pixel‐based algorithms for processing tracked 2‐dimensional ultrasound images (0.035 seconds per 2‐dimesional cross‐sectional image [76,800 pixels interpolated, or 0.46 ms/1000 pixels] and 1.05 seconds per full volume with a 1‐mm³ voxel size [4.6 million voxels interpolated, or 0.23 ms/1000 voxels]). On the basis of these results, trilinear interpolation is recommended as a fast and accurate interpolation method for rectilinear sampling of 3DUS image acquisitions, which is required to facilitate subsequent processing and display during operating room procedures such as image‐guided neurosurgery.     

Unified framework for anisotropic interpolation and smoothing of diffusion tensor images

To enhance the performance of diffusion tensor imaging (DTI)-based fiber tractography, this study proposes a unified framework for anisotropic interpolation and smoothing of DTI data. The critical component of this framework is an anisotropic sigmoid interpolation kernel which is adaptively modulated by the local image intensity gradient profile. The adaptive modulation of the sigmoid kernel permits image smoothing in homogeneous regions and meanwhile guarantees preservation of structural boundaries. The unified scheme thus allows piece-wise smooth, continuous and boundary preservation interpolation of DTI data, so that smooth fiber tracts can be tracked in a continuous manner and confined within the boundaries of the targeted structure. The new interpolation method is compared with conventional interpolation methods on the basis of fiber tracking from synthetic and in vivo DTI data, which demonstrates the effectiveness of this unified framework.     

Estimating intensity variance due to noise in registered images: applications to diffusion tensor MRI

Image registration techniques which require image interpolation are widely used in neuroimaging research. We show that signal variance in interpolated images differs significantly from the signal variance of the original images in native space. We describe a simple approach to compute the signal variance in registered images based on the signal variance and covariance of the original images, the spatial transformations computed by the registration procedure, and the interpolation or approximation kernel chosen. The method is general and could handle various sources of signal variability, such as thermal noise and physiological noise, provided that their effects can be assessed in the original images. Our approach is applied to diffusion tensor (DT) MRI data, assuming only thermal noise as the source of variability in the data. We show that incorrect noise variance estimates in registered diffusion-weighted images can affect DT parameters, as well as indices of goodness of fit such as chi-square maps. In addition to DT-MRI, we believe that this methodology would be useful any time parameter extraction methods are applied to registered or interpolated data, such as in relaxometry and functional MRI studies.     

An adaptive snake model for ultrasound image segmentation: modified trimmed mean filter, ramp integration and adaptive weighting parameters

The snake model is a widely-used approach to finding the boundary of the object of interest in an ultrasound image. However, due to the speckles, the weak edges and the tissue-related textures in an ultrasound image, conventional snake models usually cannot obtain the desired boundary satisfactorily. In this paper, we propose a new adaptive snake model for ultrasound image segmentation. The proposed snake model is composed of three major techniques, namely, the modified trimmed mean (MTM) filtering, ramp integration and adaptive weighting parameters. With the advantages of the mean and median filters, the MTM filter is employed to alleviate the speckle interference in the segmentation process. The weak edge enhancement by ramp integration attempts to capture the slowly varying edges, which are hard to capture by conventional snake models. The adaptive weighting parameter allows weighting of each energy term to change adaptively during the deformation process. The proposed snake model has been verified on the phantom and clinical ultrasound images. The experimental results showed that the proposed snake model achieves a reasonable performance with an initial contour placed 10 to 20 pixels away from the desired boundary. The mean minimal distances from the derived boundary to the desired boundary have been shown to be less than 3.5 (for CNR > or = 0.5) and 2.5 pixels, respectively, for the phantom and ultrasound images.     

Phantom-based comparison of conventional versus phase-contrast mammography for LCD soft-copy diagnosis

Purpose

Liquid crystal display (LCD) of mammograms provides soft-copy results that differ in conventional and phase contrast mammography (PCM). PCM potentially offers the highest quality of sharpness and graininess, an edge emphasis effect on the object, and the highest image resolution. However, when the image is displayed on an LCD, the resolution depends on the pixel pitch and the PCM image data must be diminished. We investigated the observed effect on spatial resolution and contrast when conventional or phase contrast mammograms are viewed on an LCD.

Methods

Using the tissue-equivalent phantom (Model 1011A), a conventional mammogram and a magnification radiography image were obtained with a PCM system. This phantom contains simulated fibers, microcalcifications, and masses. The PCM image was reduced 1/1.75 to render it consistent with life size mammography using the nearest neighbor, bilinear, and bicubic interpolation methods. The images were displayed on a five million (5M)-pixel LCD with 100 % magnification. Ten mammography technicians observed the reduction images displayed on LCDs and reported their results.

Results

In the detectability of the microcalcifications, there was no significant difference between conventional mammograms and reduced PCM images. Regarding fibers and masses, detectability using reduced images was higher than those of conventional images. The detectability using images reduced by the nearest-neighbor method was lower than those of images reduced by two other interpolation methods. Bilinear interpolation was affected by the smoothing effect, while CNR was increased. In addition, since the noise of PCM image was reduced by an air gap effect, high detectability of key image features was found.

Conclusions

Soft-copy display of phase-contrast mammograms is feasible with LCDs, while detectability of fibers and masses was best with bilinear interpolation and use of an air gap.     

Enhancement of contrast regions in suboptimal ultrasound images with application to echocardiography

In this paper we propose a novel feature-based contrast enhancement approach to enhance the quality of noisy ultrasound (US) images. Our approach uses a phase-based feature detection algorithm, followed by sparse surface interpolation and subsequent nonlinear postprocessing. We first exploited the intensity-invariant property of phase-based acoustic feature detection to select a set of relevant image features in the data. Then, an approximation to the low-frequency components of the sparse set of selected features was obtained using a fast surface interpolation algorithm. Finally, a nonlinear postprocessing step was applied. Results of applying the method to echocardiographic sequences (2-D + T) are presented. The results demonstrate that the method can successfully enhance the intensity of the interesting features in the image. Better balanced contrasted images are obtained, which is important and useful both for manual processing and assessment by a clinician, and for computer analysis of the sequence. An evaluation protocol is proposed in the case of echocardiographic data and quantitative results are presented. We show that the correction is consistent over time and does not introduce any temporal artefacts. (E-mail: djamal@robots.ox.ac.uk)     

Removal of local and biased global maxima in intensity-based registration

Intensity based registration (e.g., mutual information) suffers from a scalloping artifact giving rise to local maxima and sometimes a biased global maximum in a similarity objective function. Here, we demonstrate that scalloping is principally due to the noise reduction filtering that occurs when image samples are interpolated. Typically at a much smaller scale (100 times less in our test cases), there are also fluctuations in the similarity objective function due to interpolation of the signal and to sampling of a continuous, band-limited image signal. Focusing on the larger problem from noise, we show that this phenomenon can even bias global maxima, giving inaccurate registrations. This phenomenon is readily seen when one registers an image onto itself with different noise realizations but is absent when the same noise realization is present in both images. For linear interpolation, local maxima and global bias are removed if one filters the interpolated image using a new constant variance filter for linear interpolation (cv-lin filter), which equalizes the variance across the interpolated image. We use 2D synthetic and MR images and characterize the effect of cv-lin on similarity objective functions. With a reduction of local and biased maxima, image registration becomes more robust and accurate. An efficient implementation adds insignificant computation time per iteration, and because optimization proceeds more smoothly, sometimes fewer iterations are needed.     

Speckles Suppression Techniques for Ultrasound Images

Most medical images have a poorer signal to noise ratio than scenes taken with a digital camera, which often leads to incorrect diagnosis. Speckles suppression from ultrasound images is one of the most important concerns in computer-aided diagnosis. This article proposes two novel, robust and efficient ultrasound images denoising techniques. The first technique is the enhanced ultrasound images denoising (EUID) technique, which estimates automatically the speckle noise amount in the ultrasound images by estimating important input parameters of the filter and then denoising the image using the sigma filter. The second technique is the ultrasound image denoising using neural network (UIDNN) that is based on the second-order difference of pixels with adaptive threshold value in order to identify random valued speckles from images to achieve high efficient image restoration. The performances of the proposed techniques are analyzed and compared with those of other image denoising techniques. The experimental results show that the proposed techniques are valuable tools for speckles suppression, being accurate, less tedious, and preventing typical human errors associated with manual tasks in addition to preserving the edges from the image. The EUID algorithm has nearly the same peak signal to noise ratio (PSNR) as Frost and speckle-reducing anisotropic diffusion 1, whereas it achieves higher gains, on average—0.4 dB higher PSNR—than the Lee, Kuan, and anisotropic diffusion filters. The UIDNN technique outperforms all the other techniques since it can determine the noisy pixels and perform filtering for these pixels only. Generally, when relatively high levels of noise are added, the proposed algorithms show better performances than the other conventional filters.     

Cardiac MR Elastography: Comparison with left ventricular pressure measurement

Background

Gibbs ringing has been shown as a possible source of dark rim artifacts in myocardial perfusion studies. This type of artifact is usually described as transient, lasting a few heart beats, and localised in random segments of the myocardial wall. Dark rim artifacts are known to be unpredictably variable. This article aims to illustrate that a sub-pixel shift, i.e. a small displacement of the pixels with respect to the endocardial border, can result in different Gibbs ringing and hence different artifacts. Therefore a hypothesis for one cause of dark rim artifact variability is given based on the sub-pixel position of the endocardial border. This article also demonstrates the consequences for Gibbs artifacts when two different methods of image interpolation are applied (post-FFT interpolation, and pre-FFT zero-filling).

Results

Sub-pixel shifting of in vivo perfusion studies was shown to change the appearance of Gibbs artifacts. This effect was visible in the original uninterpolated images, and in the post-FFT interpolated images. The same shifted data interpolated by pre-FFT zero-filling exhibited much less variability in the Gibbs artifact. The in vivo findings were confirmed by phantom imaging and numerical simulations.

Conclusion

Unless pre-FFT zero-filling interpolation is performed, Gibbs artifacts are very dependent on the position of the subendocardial wall within the pixel. By introducing sub-pixel shifts relative to the endocardial border, some of the variability of the dark rim artifacts in different myocardial segments, in different patients and from frame to frame during first-pass perfusion due to cardiac and respiratory motion can be explained. Image interpolation by zero-filling can be used to minimize this dependency.