一种基于无抽样方向滤波器组的抑制噪声的医学图像增强算法

目的:针对传统的图像增强算法中存在弱边缘增强效果差、同时噪声抑制较弱等问题,本文提出的一种无抽样方向滤波器组,并将其用在医学图像增强中.方法:首先,将一维低通滤波器转换成二维低通滤波器,此滤波器再经平移、旋转等操作得到其他多方向无抽样频域滤波器,并将各频域滤波器转化成空间模板,便于使用;其次,将多尺度分析方法与无抽样方向滤波器组结合对图像进行分解得到各子带图像,对各子带图像进行统计特性分析,确定图像的强边缘、弱边缘和噪声;最后,对此三类信息不同的线性变换分别进行处理,获得增强后的图像.结果:由于无抽样方向滤波器能够较好的捕获图像的方向信息,多尺度分析能够较好区分不同的边缘信息,所以该方法的结果很好的保护了图像的边缘及细节.同时有效的去除了图像的噪声,层次结构清晰,视觉效果有了显著改善.结论:将无抽样方向滤波器与多尺度分析结合,并根据不同纹理信息的特点,对各类信息采用不同的处理,是一种行之有效的图像增强方法.     

约束参数Gabor滤波器及细胞分割

对活细胞进行精确计数，是体外抗病毒试验中测定药物抗病毒效果的重要手段。在进一步研究分析Gabor滤波器和所要分割的细胞图像特征的基础上，提出了带约束参数的Gabor滤波器，即根据Heisenberg测不准原理，用频域中高斯核的标准差来约束空域中高斯核的标准差；进而用标准差来约束Gabor滤波器中的中心频率的取值范围，达到提高效率和精度的目的。将带约束参数的Gabor滤波器用于细胞图像的增强，进而获得更高精度的分割和计数结果。对采集到的细胞图像进行大量实验，准确率达98．8％。     

一种病变眼底图像的配准方法

对于具有病变的眼底图像,血管结构不够清晰,采用基于血管分割和血管分支点、交叉点等眼底图像配准方法具有一定的局限性。为了解决这个问题,提出一种基于不变特征的眼底图像配准方法。提取眼底图像的尺度不变特征(SIFT)作为特征点,提出双边或的Best-Bin-First(BBF)算法进行特征点匹配,并根据特征点具有旋转不变性的方向特征和空间斜率及空间距离等几何特性的一致性检测去除误匹配,精化匹配特征,利用得到的匹配特征进行M估计得到眼底图像的变换关系。通过对不同程度病变的眼底图像数据进行配准实验,观察配准结果,对比匹配特征的正确匹配数量和分析衡量配准精度的均方根误差。结果表明,该方法实现了良好的细节对齐,保留了足够的正确匹配对,对实验的病变眼底图像配准成功后的均方根误差均小于1,且浮动于0.5左右,验证了方法的精确性和有效性。     

眼底数码图像血管分割预处理关键技术研究

目的本文以眼底图像为研究对象,针对眼底图像血管分割的预处理关键技术进行了深入的研究和探讨。方法首先通过RGB通道选择,显示出较为清晰的血管图像,并通过自适应直方图均衡化增强图像对比度。其次,利用灰度倒置和灰度增强,使细小血管更加清晰,并采用空域滤波对血管轮廓边缘进行增强。最后进行图像填充,去除背景中的噪声干扰。结果通过MATLAB仿真实验,获得了血管脉络清晰的眼底数码图像。结论本研究为血管的自动分割以及血管成分的定性和定量分析奠定了基础。     

免散瞳眼底图像中微动脉瘤的高效自动检测

为快速、有效地自动检测免散瞳眼底图像中的微动脉瘤,构建基于免散瞳眼底图像的糖尿病视网膜病变自动筛查系统,提出了一种简单而高效的微动脉瘤自动检测算法。在对免散瞳眼底图像G通道预处理的基础上,利用数学形态学分割提取硬性渗出和血管;并通过将二者从扩展极小值变换后的二值图像中去除而获得微动脉瘤候选区域;进而根据尺寸信息获取真正的微动脉瘤。利用该算法对两组不同质量免散瞳眼底图像进行微动脉瘤自动检测,并对检测结果进行统计分析。结果表明:两组图像检测结果精度均较高,相应指标间的相对误差均低于4%,且处理效率高(平均一幅图像的处理时间为9.7 s)。该算法能够高效地自动检测出免散瞳眼底图像中的微动脉瘤,且算法稳定可靠,具有很高的实用价值。     

基于高斯过程和Radon变换的视网膜血管管径测量

视网膜血管管径的异常变化与糖尿病、高血压等心脑血管疾病发展进程息息相关,眼底图像中视网膜血管信息的提取是计算机辅助分析和诊断相关疾病的重要步骤。本研究提出一种视网膜血管管径测量方法。首先对眼底图像进行图像预处理,然后基于高斯过程和Radon变换准确跟踪血管中心线和方向,最后利用二维高斯过程回归技术测量血管管径。在DRIVE和STARE这两个眼底图像数据库中进行测试。结果表明不论是对于曲率较小的近似直线型血管段、曲率较大的弯曲型血管段,还是对于管径发生变化的血管段,本文方法都能较好地检测出血管管径宽度,且标准差低、运算速度快。     

基于非下采样Contourlet变换的眼底图像PCA增强方法

眼底图像是眼科疾病及其他疾病的有效诊断依据,对现代医学有着很重要的价值。但是由于眼睛结构的限制,眼底图像具有对比度差、背景亮度不均的特点,给医生诊断和视网膜血管提取带来诸多不便。因此本研究提出了基于非下采样Contourlet变换的主分量分析增强方法,并应用于眼底图像的增强处理。该方法首先对图像进行非下采样Contourlet变换,获得多尺度多方向的分解系数矩阵,然后利用主分量分析方法直接估计各尺度各方向的噪声能量,根据噪声能量估计值对变换系数矩阵进行处理,最后对修正后的系数矩阵进行非下采样Contourlet逆变换得到增强图像。实验表明该方法可以达到良好的去噪增强效果,和其他方法相比,眼底图像的质量得到改善,峰值信噪比得到了提高。     

造影血管边缘检测方法研究

本文讨论了一种新的血管造影图像边缘检测方法。这一方法是首先调整正规化滤波器的正规化参数用以平滑血管造影图像，去除低于这参数尺度的细节噪声，再利用小波变换提取血管的边缘。我们的结果表明，在相当大的正规化参数范围内，有效地去除不需要的细节噪声的同时，还能很好的保证提取的血管边缘的精度。与单独使用小波变换或递推正规化滤波器相比，该方法的优点是既弥补了小波变换去除相关噪声差的弱点，又改善了正规化滤波器局部分辨率差的缺点。     

眼底数码图像处理关键技术研究

目的目前医生一般通过检眼镜等设备查看眼病患者眼底结构特征诊断疾病,工序烦琐、存在误差并受主观因素的影响,诊断效率低。因此有必要开发一款眼底数码图像处理系统配合医生诊断。方法对原始图像进行增强对比度和空域滤波的预处理操作、二值化处理、形态学处理及边缘提取和骨架提取,进而得到目标特征区域。结果本文初步研究眼底数码图像视杯、视盘和血管轮廓特征的自动提取关键技术,通过MATLAB仿真软件对特征提取关键技术进行总结和验证,归纳出提取不同眼底特征时的应对策略。结论利用数字图像处理技术可以自动识别眼底数码图像中的不同特征区域,建立眼底数码图像处理系统用于辅助医生诊断是可行的。     

眼底图像中硬性渗出物检测算法

目的利用眼底图像中硬性渗出物(hard exudates,HE)的亮度与边缘特征,提出一种基于Canny边缘检测算法与形态学重构相结合的HE自动检测方法,以解决目前算法灵敏度低、检测结果中视盘和血管的干扰等问题,对糖尿病视网膜病变(diabetic retinopathy,DR)的自动筛查具有重要意义。方法检测算法包括4个步骤。步骤一,图像预处理,主要包括RGB通道选取、基于形态学的图像对比度增强。步骤二,视网膜图像关键结构的消除,利用基于Gabor滤波的血管分割方法,消除血管边缘对HE检测的影响。将本文视杯分割算法应用在眼底图像红色通道上实现视盘自动分割,消除视盘及其边缘对HE检测的影响。步骤三,利用改进的Canny边缘检测算法和形态学重构方法对HE进行提取。步骤四,基于形态学的图像后处理,消除眼底图像边缘部分假阳性区域。最后利用该算法测试公开数据库中的40幅图像(35幅HE病变图像,5幅正常图像)。结果该算法对基于病变的灵敏性(sensitivity,SE)和阳性预测值(positive predictive value,PPV)分别为93.18%和79.26%,基于图像的灵敏性、特异性(specificity,SP)和准确率(accuracy,ACC)分别为97.14%、80.00%和95.00%。结论与其他方法对比,基于Canny边缘检测算法与形态学重构相结合的HE自动检测算法具有较好的可行性。     

Medical feature based evaluation of structuring elements for morphological enhancement of ultrasonic images

This paper investigates the use of morphology-based nonlinear filters, and performs deterministic and statistical analysis of the linear combinations of the filters for the image quality enhancement of B-mode ultrasound images. The fact that the structuring element shape greatly influences the output of the filter, is one of the most important features of mathematical morphology. The present reported work comparatively evaluates the structuring elements for morphological liver image enhancement and verifies the hypothesis that the speckles visible in US images are short, slightly ‘banana-shaped’ white lines. Initially, five different liver images were morphologically filtered using 10 different structuring elements and then the filtered images were assessed quantitatively. Image quality parameters such as peak signal-to-noise ratio, mean square error and correlation coefficient have been used to evaluate the performance of the morphological filters with different structuring elements. To endorse the observation of the quantitative analysis, the filtered images were then evaluated qualitatively, based on the image features looked into by the medical fraternity. The evaluation parameters have been taken on the basis of the suggestions made by a group of radiologists. The results of the processed images were then evaluated by a different group of radiologists. A multi-point rank order method has been used to identify small differences or trends in observation. The subjective analysis by radiologists indicates that morphological filter using line shaped structuring element with length 2 performs better than the other structuring elements. These observations were found to be in line with the observations of quantitative analysis.     

CHO数字观察器在PET图像滤波方法评估中的应用

在临床应用中需要限制扫描时间和药物剂量,这往往会使正电子发射断层扫描(PET)的图像的分辨率变低,噪声变多。为提供可供临床诊断的图像,去噪是一个必须的手段,而在重建后增加一个滤波器是目前最常用的去噪方法。因此对不同滤波器滤波效果的比较是PET图像重建中的重要环节,其中最关键的是滤波参数的选取。目前采用的信噪比(SNR)以及恢复系数(RC)等评估方法可以用来非定量地选取参数,研究者们只能凭经验选取最优参数。而通道化霍特林观察器(CHO)作为一个比较通用的数字观察器,已被用于与PET图像质量相关的各种参数的选择,如重建算法参数、系统设计参数、临床协议参数等,然而其在评估不同滤波方法对图像重建质量的影响中的应用研究还比较少。通过比较CHO计算得到的ROC(receiver operating characteristic)曲线下面积(area under the ROC curve,AUC),选择两种常用的滤波器(即高斯滤波器和非局部均值(Non-Local Mean, NLM)滤波器)的最优参数,并评估它们在PET中的滤波效果。结果表明,对于13 mm球体,σ为1.1~1.4的高斯滤波器和f为0.5~0.9的NLM滤波器可以达到最大的检测能力值,而对于10 mm球体,σ为1.4~2.0的高斯滤波器和f为0.5~0.9的NLM滤波器可以达到最大的检测能力值。虽然两个滤波器所对应的AUC值都能高达0.9,但是NLM滤波器的AUC值高于高斯滤波器。通过IEC图像和病人图像也能发现,NLM滤波后的PET图像中的亮点比高斯滤波的更加清晰,噪声更少。该结论和传统滤波器评估方法得到的结论一致,这说明在PET的病灶检测任务中,CHO能够准确地比较这两种滤波器的性能。     

Detection of the Optic Nerve Head in Fundus Images of the Retina with Gabor Filters and Phase Portrait Analysis

We propose a method using Gabor filters and phase portraits to automatically locate the optic nerve head (ONH) in fundus images of the retina. Because the center of the ONH is at or near the focal point of convergence of the retinal vessels, the method includes detection of the vessels using Gabor filters, detection of peaks in the node map obtained via phase portrait analysis, and an intensity-based condition. The method was tested on 40 images from the Digital Retinal Images for Vessel Extraction (DRIVE) database and 81 images from the Structured Analysis of the Retina (STARE) database. An ophthalmologist independently marked the center of the ONH for evaluation of the results. The evaluation of the results includes free-response receiver operating characteristics (FROC) and a measure of distance between the manually marked and detected centers. With the DRIVE database, the centers of the ONH were detected with an average distance of 0.36 mm (18 pixels) to the corresponding centers marked by the ophthalmologist. FROC analysis indicated a sensitivity of 100% at 2.7 false positives per image. With the STARE database, FROC analysis indicated a sensitivity of 88.9% at 4.6 false positives per image.     

Selection of biorthogonal filters for image compression of MR images using wavelet packets

We present an analysis of different filter banks for the compression of magnetic resonance (MR) images of the human brain using wavelet packets based on biorthogonal filters. Initially, peak signal to noise ratio (PSNR) and normalized root mean square (RMS) error criteria are calculated for a series of images compressed with a 33:1 ratio, using filter banks based on biorthogonal wavelet packets. The results lead us to choose a few of these filter banks as optimal for image compression. One of these filters is employed to compress several images at four different compression ratios: 12.5:1, 25:1, 37.5:1 and 50:1. The quality of these images was evaluated by visual analysis by a group of seven experts who graded image quality on a 0-7 scale. Results show that using these filters, we can compress images to a rate of around 30:1 without introducing noticeable differences. Other applications for these filters are currently under study and include the compression/fusion of MR image stacks in order to obtain even better reductions in the amount of data needed to reconstruct complete MRI studies.     

Compared study of micro-aggregates filters (output, filtration pressure and study of the deposit with scanning electron microscopy) (author's transl)]

Special filters for the elimination of cellular aggregates were conceived especially to prevent pulmonary complications in patients shocked particularly after massive transfusions as well as to prevent cerebral complications after extra-corporeal circulation. The performances and the conditions of use of these filters (perfusion output, change of the filter) were considered. Four filters were carefully studied: the Fenwal filter, the Swank filter, the Biotest filter and the intersep Johnson and Johnson filter. The following studies were carried out for each filter: the perfusion output (of three bottles filtered one after another with or without pressure); the filtration pressure (Swank technique) of the whole blood up-and-down filter; the viscosity and various hematologic parameters (NF, hemoglobin and gobular ATP); finally, the deposit was studied morphologically with scanning electron microscopy. It appeared that a good capacity of aggregates elimination (decrease of the filtration pressure of 95%) was often linked to a feeble output and the necessity of replacing the filter. The efficacy of the Fenwal filter, the Swank filter, the Biotest filter and the intersept filter was practically the same, but the Intersept filter provided the best output with an equal filtration capacity. The other parameters remained about the same. The deposits included fibrinogen, deformed red blood corpuscules (echinocytes), leucocytes and platelets.     

Enhancing obstetric and gynecology ultrasound images by adaptation of the speckle reducing anisotropic diffusion filter

OBJECTIVE: So far there is no ideal speckle reduction filtering technique that is capable of enhancing and reducing the level of noise in medical ultrasound (US) images, while efficiently responding to medical experts' validation criteria which quite often include a subjective component. This paper presents an interactive tool called evolutionary speckle reducing anisotropic diffusion filter (EVOSRAD) that performs adaptive speckle filtering on ultrasound B-mode still images. The medical expert runs the algorithm interactively, having a permanent control over the output, and guiding the filtering process towards obtaining enhanced images that agree to his/her subjective quality criteria. METHODS AND MATERIAL: We employ an interactive evolutionary algorithm (IGA) to adapt on-line the parameters of a speckle reducing anisotropic diffusion (SRAD) filter. For a given input US image, the algorithm evolves the parameters of the SRAD filter according to subjective criteria of the medical expert who runs the interactive algorithm. The method and its validation are applied to a test bed comprising both real and simulated obstetrics and gynecology (OB/GYN) ultrasound images. RESULTS: The potential of the method is analyzed in comparison to other speckle reduction filters: the original SRAD filter, the anisotropic diffusion, offset and median filters. Results obtained show the good potential of the method on several classes of OB/GYN ultrasound images, as well as on a synthetic image simulating a real fetal US image. Quality criteria for the evaluation and validation of the method include subjective scoring given by the medical expert who runs the interactive method, as well as objective global and local quality criteria. CONCLUSIONS: The method presented allows the medical expert to design its own filters according to the degree of medical expertise as well as to particular and often subjective assessment criteria. A filter is designed for a given class of ultrasound images and for a given medical expert who will later use the respective filter in clinical practice. The process of designing a filter is simple and employs an interactive visualization and scoring stage that does not require image processing knowledge. Results show that filters tailored using the presented method achieve better quality scores than other more generic speckle filtering techniques.     

Nonlinear filters applied on computerized axial tomography: theory and phantom images.

New nonlinear image processing techniques, in particular smoothing based on the understanding of the image, may create computerized tomography (CT) images of good quality using less radiation. Such techniques may be applied before the reconstruction and particularly after it. Current CT scanners use strong linear low-pass filters applied to the CT projections, reducing noise but also deteriorating the resolution of the image. The method in this study was to apply a weak low-pass filter on the projections, to perform the reconstruction, and only then to apply a nonlinear filter on the image. Various kinds of nonlinear filters were investigated based on the fact that the image is approximately piecewise constant. The filters were applied with many values of several parameters and the effects on the spatial resolution and the noise reduction were evaluated. The signal-to-noise ratio of a high-contrast phantom image processed were compared with the nonlinear filter, with the SNR of the phantom images obtained with the built-in CT linear filters in two scanning modes, the normal and the ultra high resolution modes. It was found that the nonlinear filters improve the SNR of the image, compared to the built-in filters, about three times for the normal mode and twice for the UHR scanning mode. The most successful filter on low-contrast phantom image was applied and it also seems to lead to promising results. These results seem to show that applying nonlinear filters on CT images might lead to better image quality than using the current linear filters.(ABSTRACT TRUNCATED AT 250 WORDS)     

Computer-aided Diagnosis of Proliferative Diabetic Retinopathy via Modeling of the Major Temporal Arcade in Retinal Fundus Images

Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate diagnosis and treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided semiautomated modeling and measurement of the openness of the MTA based on Gabor filters for the detection of retinal vessels, morphological image processing, and a form of the generalized Hough transform for the detection of parabolas. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 11 normal individuals and 11 patients with PDR in the present pilot study on potential clinical application. A method of arcade angle measurement was used for comparative analysis. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A _z = 0.87, 0.82, and 0.80, respectively. The proposed methods are expected to facilitate quantitative analysis of the architecture of the MTA, as well as assist in detection and diagnosis of PDR.     

Prostate Tissue Texture Feature Extraction for Suspicious Regions Identification on TRUS Images

In this work, two different approaches are proposed for region of interest (ROI) segmentation using transrectal ultrasound (TRUS) images. The two methods aim to extract informative features that are able to characterize suspicious regions in the TRUS images. Both proposed methods are based on multi-resolution analysis that is characterized by its high localization in both the frequency and the spatial domains. Being highly localized in both domains, the proposed methods are expected to accurately identify the suspicious ROIs. On one hand, the first method depends on a Gabor filter that captures the high frequency changes in the image regions. On the other hand, the second method depends on classifying the wavelet coefficients of the image. It is shown in this paper that both methods reveal details in the ROIs which correlate with their pathological representations. It was found that there is a good match between the regions identified using the two methods, a result that supports the ability of each of the proposed methods to mimic the radiologist’s decision in identifying suspicious regions. Studying two ROI segmentation methods is important since the only available dataset is the radiologist’s suspicious regions, and there is a need to support the results obtained by either one of the proposed methods. This work is mainly a preliminary proof of concept study that will ultimately be expanded to a larger scale study whose aim will be introducing an assisting tool to help the radiologist identify the suspicious regions.