基于小波变换的医学超声图像去噪及增强方法

目的探求一种基于小波变换的医学超声图像去噪及增强方法。方法提出了一种基于小波分析理论的医学超声图像噪声的综合抑制方法,首先对医学超声图像进行对数变换,将乘性噪声变成加性噪声;然后进行多尺度小波变换,将图像分解成一系列不同尺度上的小波系数,对变换后不同尺度的高频子图像进行非线性小波软阈值处理,阈值处理后的高频子图像进行增强;最后,经小波逆变换和指数变换恢复去噪后图像。结果原图像中斑纹噪声被有效去除,图像边缘细节得以保留。结论该方法可有效保留细节信号,极大限度地去除斑纹噪声。     

MRI图像的小波融合处理

背景:小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述.目的:用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像.方法:图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合.通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法.结果与结论:不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位.对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好.提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断.     

基于维纳滤波与小波相融合的超声医学图像去噪方法

目的 探求一种有效的超声医学图像去噪方法.方法 在分析维纳滤波和基于自适应前处理的多尺度小波非线性阈值斑点噪声抑制方法(MSSNT-A)的基础上,提出一种基于维纳滤波与MSSNT-A相融合的超声医学图像去噪方法.利用该方法首先对加噪图像分别进行维纳滤波和MSSNT-A去噪.然后提取维纳滤波处理后的图像边缘,再将其与MSSNT-A去噪后的图像的_柑应像素点进行融合,得到去噪图像.结果 有效地去除了斑点噪声,图像的细节得到保留.结论 与维纳滤波和MSSNT-A方法相比,该方法在有效去除斑点噪声的同时,很好地保留了图像边缘和图像细节信息.     

MRI图像的小波融合处理

背景：小波图像融合是将两幅图像融合在一起,以获取对同一场景的更为精确、全面、可靠的图像描述。目的：用小波变换图像融合技术融合MRI脑梗死图像,以恢复缺损图像。方法：图像融合的主要机制是利用二维小波分析法对MRI脑梗死图像进行小波分解,并对高低频信号采用多种融合方式进行融合。通过对比不同融合方式后的效果图,找出最适合本部位MRI图像的融合方法。结果与结论：不同方式的融合技术能成功修复不同的缺损部位,多种融合方式的合适组合能完全修复多处缺失部位。对于文中给出的MRI脑梗死图像,采用最小值融合方式的融合效果最好。提示使用二维小波分析法处理医学图像,简便快捷,能有效改善图像的视觉效果,辅助临床诊断。     

基于小波变换的PET/CT图像融合算法研究进展

随着图像融合技术在影像学领域的不断发展,越来越多新的影像技术及成像设备出现。PET/CT将PET和CT两种模态的医学图像进行融合,优势互补,提高了医师诊断疾病的效率及准确率。小波变换在医学图像融合中有重要作用,基于小波变换的算法使融合后图像的细节更加清晰而易于诊断,已成为近年来医学图像融合领域中研究的重点。本文对基于小波变换的PET/CT图像融合算法的研究进展进行综述。     

小波先验在OSL重建算法中的应用

背景:MAP(最大后验)统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的.目的:根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声.方法:实验从“变换域"的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建.结果与结论:基于小波先验的OSL算法比ML-EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚.     

基于像素融合的Curvelet医学超声图像降噪方法

目的：探求基于Curvelet变换的医学超声图像降噪的有效方法。方法：分别对超声图像进行Curvelet和隐Markov树小波降噪，再采用模糊分区方法对结果图像进行像素融合。结果：实现了基于像素融合的Curvelet医学超声图像降噪。结论：对超声图像的降噪实例表明该方法有效提高了图像的视觉效果，明显抑制了伪影。     

基于Contourlet的医学图像融合技术探讨

针对不同医学影像设备获得的多源图像信息有效融合和综合利用的问题,提出一种基于Contourlet区域特性的医学图像融合算法——CRSIF算法,借助于Contourlet变换的优良特性,在Contourlet变换域使用加权平均和选择方式实现频域系数的有效融合,对低频子带采用局部加权能量作为评价标准,高频子带采用区域加权Contourlet对比度的树型结构设计,以满足区域的融合规则。对CT/MR脑部医学图像的仿真分析表明,该算法可克服传统规则下融合图像不连续及产生毛刺和斑点的缺陷,使融合图像与人类视觉系统的感知特性相吻合。     

基于小波极大模值信息的医学图像去噪方法

目的 利用小波变换进行医学图像去噪.方法 通过分析二进小波变换下小波极大模值的特点,即信号的极大模值往往会大于噪声的极大模值,而且噪声的极大模值会随着尺度增大而急剧减少,信号的极大模值却改变很小,由此构造了更有效的去噪准则,即根据不同尺度上的极大模值信息,选择不同的域值来滤除噪声.结果 应用该方法进行医学图像去噪,能保持较高的峰值信噪比、图像细节和边缘特征以及图像清晰度.结论 基于小波极大模值信息的去噪方法能有效地降低医学图像中的噪声.     

小波先验在OSL重建算法中的应用

背景：MAP（最大后验）统计重建方法可以在重建过程中引入合适的先验知识达到去除噪声的目的。目的：根据小波系数的统计特性及能量平衡的原理对高频信息做相应的处理,并将多尺度的小波先验应用到OSL重建算法中以去除噪声。方法：实验从“变换域”的思想出发,在小波域上根据小波系数的统计特性及能量平衡原理对不同尺度的高频信息做相应的处理,并利用处理后的小波系数进行小波重建。结果与结论：基于小波先验的OSL算法比ML—EM算法重建的图像与测试模型的误差变小、相关性变大、噪声变少,重建图像变得比较平滑,视觉效果较清楚。     

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

Purpose

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

Methods

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

Results

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

Conclusion

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

     

基于小波变换的数字胸片增强

目的 研究一种基于小波变换的数字胸片图像增强新算法.方法 小波分解后,首先利用小波阈值法进行去噪预处理,然后对高频分量采用非线性增强,对低频分量采用反锐化掩模增强方法,通过小波反变换重构出增强后的图像.结果 通过对传统增强方法和本文提出的小波增强新方法进行实验对比,验证了本文算法对数字胸片图像有较好的增强效果.结论 对于分辨力低、噪声干扰严重、光照不均的数字胸片图像,本文提出的基于小波变换的增强新方法可保留图像细节信息,同时有效去除噪声.     

基于深度学习与多模态医学影像融合识别阈下抑郁患者

目的 基于深度学习（DL）卷积神经网络（CNN）算法，利用医学影像数据实现识别阈下抑郁（StD）患者。方法 对56例StD患者和70名正常人采集MRI和fMRI数据，分别输入所构建的CNN，利用网络融合技术对2种不同模态数据进行综合分析，得到分类结果；最后调整网络结构与模型参数，实现分类效果最优化。结果 单独MRI数据模型分类精度为73.02%，单独fMRI数据模型分类精度为65.08%；2种模态结合，最终分类精度升至78.57%。结论 利用DL可识别StD患者与正常人；采用多种模态输入法可提高分类准确度。     

Multiscale 3D shape representation and segmentation with applications to hippocampal/caudate extraction from brain MRI

Extracting structure of interest from medical images is an important yet tedious work. Due to the image quality, the shape knowledge is widely used for assisting and constraining the segmentation process. In many previous works, shape knowledge was incorporated by first constructing a shape space from training cases, and then constraining the segmentation process to be within the learned shape space. However, such an approach has certain limitations due to the number of variations, eigen-shapemodes, that can be captured in the learned shape space. Moreover, small scale shape variances are usually overwhelmed by those in the large scale, and therefore the local shape information is lost. In this work, we present a multiscale representation for shapes with arbitrary topology, and a fully automatic method to segment the target organ/tissue from medical images using such multiscale shape information and local image features. First, we handle the problem of lacking eigen-shapemodes by providing a multiscale shape representation using the wavelet transform. Consequently, the shape variances existing in the training shapes captured by the statistical learning step are also represented at various scales. Note that by doing so, one can greatly enrich the eigen-shapemodes as well as capture small scale shape changes. Furthermore, in order to make full use of the training information, not only the shape but also the grayscale training images are utilized in a multi-atlas initialization procedure. By combining such initialization with the multiscale shape knowledge, we perform segmentation tests for challenging medical data sets where the target objects have low contrast and sharp corner structures, and demonstrate the statistically significant improvement obtained by employing such multiscale representation, in representing shapes as well as the overall shape based segmentation tasks.     

Visual enhancement of digital ultrasound images: wavelet versus Gauss–Laplace contrast pyramid

Purpose Noise is the principal factor which hampers the visual quality of ultrasound images, sometimes leading to misdiagnosis. Speckle noise in ultrasound images can be modeled as a random multiplicative process. Speckle reduction techniques were applied to digital ultrasound images to suppress noise and improve visual quality. Rationale Previous reports indicate that wavelet filtering performs best for speckle reduction in digital ultrasound images. Reportes on x-ray images compared wavelet filtering with Laplace-Gauss contrast enhancement (LGCE) showed that the LCGE performed better. As LGCE was never been applied to Ultrasound images, this study compared two filtering approaches for speckle reduction on digital ultrasound images. Methods Two methods were implemented and compared. The first method uses the wavelet soft threshold (WST) approach for enhancement. The second method is based on multiscale Laplacian-Gaussian contrast enhancement (LGCE). LGCE is derived from the combination of a Gaussian pyramid and a Laplacian one. Contrast enhancement is applied on local scale by using varying sizes of median filter. Results The two methods were applied to synthetic and real ultrasound images. A comparison between WST and LGCE methods was performed based on noise level, artifacts and subjective image quality. Conclusion WST visual enhancement provided better results than LGCE for selected ultrasound images.     

医学图像背景分割

目的 探讨医学图像背景分割的方法.方法 首先采用常规的自适应阈值方法对图像背景进行分割,但效果不理想;接着对医学图像的特点进行分析,最后采用背景拟合设定阈值进行分割.结果 实现了医学图像背景的分割.结论 实验表明上述方法能够非常有效地分割医学图像背景.     

基于各向异性扩散和小波变换的磁共振图像降噪和增强方法

降噪是医学图像处理中一个非常重要的问题,传统去噪方法在降低噪声的同时会模糊图像的边缘,各向异性扩散滤波在降低图像噪声的同时能够使图像的边缘得到保持.利用小波变换可以对图像进行多尺度分解,使我们可以在不同尺度上对图像进行处理.本文利用各向异性扩散滤波对MRI图像进行降噪,然后利用平稳小波变换对图像进行增强处理.实验结果表明,该方法在有效去除噪声的同时能够增强图像的细节,有效地提高了图像的质量.     

基于特征配准的[PET+CT＋MR] “三机三维”影像一体化融合尝试

目的尝试以基于图像特征的二维图像配准方法,实现PET、MR和CT异机图像之间的精确三维融合。方法输入PET/CT/MR原始数据后,采用数字化格式转换,设计"9点3面"立体定位法进行配准,在Mimics实时工作站按照信息交互自动融合模式并通过信号叠加技术施行图像融合。结果以头、胸、腹为实例交叉试验[CT+MR]、[PET+MR]、[PET+CT]和[PET+CT+MR]立体图像的异机融合,生成了同时分辨软硬组织病灶性质和位置的互补影像。结论在现阶段,此种异机融合方法是对同机成像功用的必要补充。