基于小波统计模型的医学超声图像去噪方法研究

超声图像中固有的斑点噪声严重降低图像的可解译程度,影响了后续的图像分析和诊断.因此,抑制相干斑噪声一直是医学超声图像预处理中一个关键性问题.本研究通过对含斑图像做对数变换和冗余小波分解,提出了一种基于Bayesian估计的小波域局部自适应性去斑算法.将斑点噪声和有用信号的小波系数分别建模为瑞利分布和拉普拉斯分布,利用最大后验概率(MAP)准则得到了一种解析的Bayesian估计表达式;进一步通过邻域窗口估计模型参数,使算法具有局部自适应性.实验仿真表明,该算法简单有效,在滤除超声图像斑点噪声的同时,较好地保持了图像的细节特征,其性能优于空间域滤波和传统的小波去噪算法.     

基于小波的医学超声图像斑点噪声抑制方法

斑点噪声是超声图像中固有的噪声。本文提出了一种新的去除斑点噪声的方法，这种方法结合中值滤波和多尺度非线性小波软阈值的优点，首先把原网像进行对数转换，然后把对数转换后的图像进行中值滤波处理，从而把转换后的图像分成两部分，对每一部分进行小波分析，假设小波系数服从广义高斯分布（GGD），利用小波系数的统计特性估计出各个部分各个尺度的阈值，最后用软阈值方法对上述两部分分别去噪。实验结果表明，本文提出的方法在有效去除斑点噪声方面，优于中值滤波，维纳滤波和多尺度非线性阈值算法（MSSNT-A）。     

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....     

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.     

基于小波变换的血管内超声图像血流斑点噪声抑制研究

血管内超声成像已经越来越广泛地应用到冠心病的诊断和介入治疗中.为了提高图像分辨率必须增加超声频率,使得血流斑点噪声也显著增强,降低了管腔和管壁的对比度,增加了识别管壁与周围组织的难度,给病情的诊断和治疗带来了不便.本研究结合小波变换域软阈值滤波法和半软阈值滤波法,并设计了一种局部阈值来实现血流斑点噪声抑制.实验结果表明该方法在抑制斑点噪声的同时保留了图像的边缘,增强了管腔和管壁的哪对比度,有助于识别管壁和周围组织.     

基于血管内超声图像时空相关性的血流斑点去噪方法

血管内超声成像的优越成像方式使得它越来越广泛地被应用到冠心病的诊断和介入治疗中。但随着超声频率的提高，血流分子回波信号（即血流斑点噪声）也显著增强，这会降低管腔和管壁的对比度，加大医生辨别、测量管腔和斑块几何参数及物理参数的难度。我们提出了一种新颖的去噪方法，它利用血管内超声图像在时间、空间上的相关信息，即组织在时间、空间上的变化比血流小这一事实。实验结果表明该方法能显著地去除斑点噪声，增强管腔和管壁的对比度，更好地帮助医生区别血管壁和周围组织。     

DSA图像中运动伪影的消除方法

由于病人存在着各种运动(如呼吸、肌肉运动、心脏运动、设备噪声),在成像过程中常会造成图像上出现伪影,干扰医生的正常诊断,为消除这种伪影,本文提出一种基于图像配准思想的全自动消除伪影的方法,该方法能够自动消除DSA图像中的大部分运动伪影,使DSA图像得到较好的增强,并为后面的血管分割和三维重建提供便利,是一种快速有效的方法.     

基于活动轮廓模型的序列超声图像的心内壁运动跟踪方法

提出一种对心脏序列超声图像中的心内壁进行运动跟踪的方法,采用活动轮廓模型将前一帧图像中snake的停留位置作为当前帧snake的初始位置,选择适当的能量函数,使能量函数最小snake变形得到当前时刻的心内壁轮廓,实验结果论证了该算法的可行性.     

Estimation of Motion in Angiographic Images

The effect of organ and tissue motion on X-ray image quality in angiography is discussed. It is shown that estimation of such motion may benefit the diagnostic value of images in several ways. The block-matching method is described. This method provides information on motion in the form of an offset vector field. The modifications of the block-matching algorithm described in this work include static region segmentation based on noise estimation and application of an additional flicker coefficient that improves the confidence of the method in case of changes in illumination.     

Detecting Stripe Artifacts in Ultrasound Images

Brain perfusion diseases such as acute ischemic stroke are detectable through computed tomography (CT)-/magnetic resonance imaging (MRI)-based methods. An alternative approach makes use of ultrasound imaging. In this low-cost bedside method, noise and artifacts degrade the imaging process. Especially stripe artifacts show a similar signal behavior compared to acute stroke or brain perfusion diseases. This document describes how stripe artifacts can be detected and eliminated in ultrasound images obtained through harmonic imaging (HI). On the basis of this new method, both proper identification of areas with critically reduced brain tissue perfusion and classification between brain perfusion defects and ultrasound stripe artifacts are made possible.     

SVM-Based CAC System for B-Mode Kidney Ultrasound Images

The present study proposes a computer-aided classification (CAC) system for three kidney classes, viz. normal, medical renal disease (MRD) and cyst using B-mode ultrasound images. Thirty-five B-mode kidney ultrasound images consisting of 11 normal images, 8 MRD images and 16 cyst images have been used. Regions of interest (ROIs) have been marked by the radiologist from the parenchyma region of the kidney in case of normal and MRD cases and from regions inside lesions for cyst cases. To evaluate the contribution of texture features extracted from de-speckled images for the classification task, original images have been pre-processed by eight de-speckling methods. Six categories of texture features are extracted. One-against-one multi-class support vector machine (SVM) classifier has been used for the present work. Based on overall classification accuracy (OCA), features from ROIs of original images are concatenated with the features from ROIs of pre-processed images. On the basis of OCA, few feature sets are considered for feature selection. Differential evolution feature selection (DEFS) has been used to select optimal features for the classification task. DEFS process is repeated 30 times to obtain 30 subsets. Run-length matrix features from ROIs of images pre-processed by Lee’s sigma concatenated with that of enhanced Lee method have resulted in an average accuracy (in %) and standard deviation of 86.3 ± 1.6. The results obtained in the study indicate that the performance of the proposed CAC system is promising, and it can be used by the radiologists in routine clinical practice for the classification of renal diseases.     

Motion Artefact Reduction in Dual-Energy Radiography

Biomedical Engineering - Dual-energy radiography is a relatively simple but powerful tool of X-ray diagnosis. It increases the diagnostic value of radiographic examination by generating separate...     

基于提升格式整数小波变换的超声图像压缩算法

本文提出了一种基于提升格式整数小波变换和改进的SPIHT编码(多级树集合分裂算法)的医学超声图像压缩算法.在压缩对象选择和小波变换方面充分考虑了超声扫描线图像的分辨率特性.与基于Mallat小波变换的标准SPIHT编码算法相比,本文算法在压缩比和重建图像峰值信噪比至少不降低的情况上,运算时间不到前者的40％,内存消耗也大大减小,因而更适合于实时图像压缩.     

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.     

一种快速的全自动超声子宫图像分割算法

使用超声成像进行子宫节育环检查工作已在我国广泛地开展,利用图像识别技术进行计算机辅助诊断对于减轻检查人员工作负担意义十分明显,其中图像分割部分的主要目标则是快速地全自动分割开图中的几个主要器官及节育环。本研究提出了一种快速的全自动子宫图像分割算法。该算法包括以下三个主要步骤:首先运用BP神经网络处理图像整体灰度分布获取基准分割阈值;其后使用超声图像斑点噪声统计特征进行同质区域判别,并根据局部灰度分布自适应调整分割阈值;最后使用数学形态学算子对分割效果做进一步的改善。基于由1200幅超声子宫图像组成的图像库,对所提算法与最大类别方差法、SNAKE活动轮廓模型等数种常用分割算法进行了性能比较,实验结果表明所提算法在速度与准确程度两方面均表现良好,平均耗时为0.93s/幅,准确程度达到了94%。本算法无需人工干预,分割速度快,分割准确程度能够被临床医生所接受,可以用作超声子宫图像辅助诊断系统的图像分割部分,具有很好的应用前景。     

基于Mask R-CNN的超声图像中胎儿头围测量方法

胎儿头围是产前超声检查中评价胎儿生长发育最重要的生物特征之一,但手工测量耗时费力且存在操作者的误差.对此,根据超声图像中胎儿头部接近椭圆形状的特征,提出头围测量损失函数.在Mask R-CNN的分割分支后,利用ElliFit算法对分割掩膜进行椭圆拟合,用Ramanujan公式计算拟合椭圆周长作为头围测量值,将头围真实值...     

Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images

The purpose of this study is to evaluate transfer learning with deep convolutional neural networks for the classification of abdominal ultrasound images. Grayscale images from 185 consecutive clinical abdominal ultrasound studies were categorized into 11 categories based on the text annotation specified by the technologist for the image. Cropped images were rescaled to 256?×?256 resolution and randomized, with 4094 images from 136 studies constituting the training set, and 1423 images from 49 studies constituting the test set. The fully connected layers of two convolutional neural networks based on CaffeNet and VGGNet, previously trained on the 2012 Large Scale Visual Recognition Challenge data set, were retrained on the training set. Weights in the convolutional layers of each network were frozen to serve as fixed feature extractors. Accuracy on the test set was evaluated for each network. A radiologist experienced in abdominal ultrasound also independently classified the images in the test set into the same 11 categories. The CaffeNet network classified 77.3% of the test set images accurately (1100/1423 images), with a top-2 accuracy of 90.4% (1287/1423 images). The larger VGGNet network classified 77.9% of the test set accurately (1109/1423 images), with a top-2 accuracy of VGGNet was 89.7% (1276/1423 images). The radiologist classified 71.7% of the test set images correctly (1020/1423 images). The differences in classification accuracies between both neural networks and the radiologist were statistically significant (p?<?0.001). The results demonstrate that transfer learning with convolutional neural networks may be used to construct effective classifiers for abdominal ultrasound images.     

发射端频率复合弹性成像的仿真研究

Objective No reports has been found to date on whether frequency compounding can improve elastographic image signal to noise ratio (SNRe) and how it affects elastogram performance.In this paper simulations investigation was carried out on transmit-side frequency compounding (TSFC)for elastography.Methods 50 mm×50 mm tissue model was simulated with two round hard inclusions of 10mm diameter uniformly distributed along the tissue central axial line,and their elasticity modulus were 10 times of the background.Then simulation of 3.5 MHz、5 MHz and 7.5 MHz probes were introduced to form compression elastography of the double-lesion model by quasi-static compression method (applied strain 1%).Then,sub-elastograms obtained by the combination of 3.5 MHz and 5 MHz,3.5 MHz and 5 MHz,3.5 MHz and 7.5 MHz were compounded,respectively.Results Before compounding,signal to noise ratio (SNRe) of the various sub-elastograms were 8.42,9.62,10.73,respectively,contrast to noise ratio (CNRe) were 11.35,14.82,18.37,respectively and axial resolutions were 9.83,9.82,9.81.After compounding elastograms,the SNRe were 11.82,13.05,19.45,CNRe were 22.31,27.63,56.12,while axial resolutions were 9.83,9.83,9.83.Conclusion Frequency compounding elastograms have higher SNRe and CNRe than any sub-elastogram before compounding and have no axial resolution loss.The TSFC can improve elastogram performance efficiently and frequency compounding for elastography enhancement is feasible.     

发射端频率复合弹性成像的仿真研究

目的 目前,有关频率复合技术能否提高弹性成像的信噪比以及对弹性图像性能的影响尚无文献报道,本研究对发射端频率复合技术应用于弹性成像进行了仿真研究.方法 仿真一50 mm×50 mm含2个圆形硬包容物的组织,2包容物直径均为10 mm且均匀分布在组织中轴线上,其弹性模量均为背景组织的10倍;然后仿真使用3.5,5,7.5 MHz频率探头对该组织区域分别进行准静态压缩弹性成像（组织压缩量为1%）;最后将3.5 MHz和5 MHz,3.5 MHz和7.5 MHz,3.5 MHz、5 MHz和7.5 MHz频率子图像分别进行复合.结果 复合前各子弹性图像的信噪比（SNRe）分别为8.42、9.62、10.73,对比度噪声比（CNRe）为11.35、14.82、18.37,轴向分辨率为9.83、9.82、9.81;复合后图像的信噪比分别为11.82、13.05、19.45,对比度噪声比为22.31、27.63、56.12,轴向分辨率为9.83、9.83、9.83.结论 复合后的图像比复合前各频率子图像的信噪比、对比度噪声比均有明显提高,轴向分辨率几乎没有损失;使用频率复合技术能有效改善弹性图像的性能,证实了发射端频率复合弹性成像技术的可行性.