一种基于小波变换的医学图像量化编码算法的研究

医学图像压缩是远程医疗和PACS系统中的重要研究课题，研究了小波子带图像系数的统计分布，发现小波子带图像系数分布和拉普拉斯分布非常相似，继而提出了一种基于其统计特征的图像量化编码算法，该算法以小波子带图像样本标准差为选择量化编码阈值的重要依据。实验表明，该算法具有计算简单，不同阈值范围待编码系数可预测以及易于获得较高压缩效率的优点，在远程医疗和PACS系统等领域的医学图像压缩中有重要的潜在应用价值。     

基于小波变换统计特征的图像压缩算法的研究

图像能量的统计分布是图像压缩处理的重要依据，在研究小波子带图像统计特性的基础上，提出了一种新的基于小波子带图像统计特征和人眼视觉特性的图像量化编码算法，实验证明，该算法具有计算简单，压缩效率较高的特点。     

基于非下采样轮廓波变换的多模态医学图像融合

由于基于小波变换的图像融合方法仅在水平、垂直、对角线三个方向对图像的高频信息进行分解,易造成图像轮廓的不连续性,而基于非下采样轮廓波变换(NSCT)的融合方法能够对图像中的高频信息进行多方向分解。本文基于NSCT对多模态医学图像进行融合,以区域能量平均加权法作为NSCT变换后低频子带的融合规则,区域能量中心加权作为高频内层带通子带的系数融合规则,外层带通子带则采用区域能量最大的系数融合规则。通过对精确配准的头部PET、CT、MR图像的融合实验,借助清晰度、信息熵、联合熵等指标进行客观评价,‘prewitt’算子提取融合图像的边缘信息进行主观评价,验证该算法在视觉效果、信息含量、实时性和长轮廓表达上的优势。相较于其他文献中提出的算法,本文算法得到的融合结果信息丰富程度提升约7%、清晰度提升约31.7%、程序运行时间缩短一半,应用前景可观。     

基于Contourlet变换和PCNN的CT图像椎体解剖轮廓特征提取方法的研究

提出一种新的基于Contourlet变换和脉冲耦合神经网络(PCNN)的医学图像解剖轮廓特征提取算法。首先对原始椎体CT图像进行Contourlet变换,得到能稀疏表示图像边缘以及方向信息的子带和低频子带;然后结合PCNN对低频子带进行边缘轮廓细节提取,最后利用处理后的所有子带系数,通过Contourlet逆变换,提取出图像的边缘轮廓。实验将本算法提取的结果与Canny算子、区域生长法以及结合小波变换和PCNN的算法提取的图像边缘轮廓进行比较,结果表明新算法能够有效的实现医学图像解剖结构轮廓特征的提取。     

基于NSST变换和PCNN的医学图像融合方法

目的:融合PET/CT/MRI医学图像,使结果图像尽可能包含更多边缘和纹理特征等信息,以更好地区分病变、肿瘤与正常组织器官,为疾病诊断提供更多的有用信息。方法:提出一种基于非下采样剪切波变换(NSST)和脉冲耦合神经网络(PCNN)模型的融合方法。首先,根据图像局部区域能量和,对图像NSST低频系数进行加权融合;然后,根据PCNN神经元的点火次数,选择图像NSST高频方向系数;最后,通过逆NSST变换,得到融合后的图像。结果:分别对7组MRI/PET和CT/PET图像进行融合实验,其结果图像具有很好的视觉效果,且在互信息、边缘相似性、梯度相似性及空间频率4个指标综合评价中较其它算法更优。结论:本方法可以自适应捕获边缘和纹理信息,具有良好的融合效果。     

多尺度MRI断层图像特征提取新技术研究

由于医学图像本身信号噪声大，边缘呈弱信号特征，用传统的图像边缘检测算法提取图像特征常会将图像中的噪声作为边缘提取，不能准确地反映医学图像中有价值的信息(如病灶大小等)。现提出一种改进算法，此法以现有的小波模极大值特征提取算法为基础，利用模糊理论确定隶属函数，提取弱信号边缘并用多尺度融合理论边缘点合成。结果表明，此方法在提取MRI图像特征的同时，可有效地抑制噪声，有助于剔除图像的伪边缘，准确定位图像边缘信息，有利于图像分割重建，便于医生根据图像确定病灶或组织的位置大小等。     

图像增强下基于生成对抗网络和卷积神经网络的CT与MRI融合方法

针对多模态医学图像融合中的重要特征丢失、细节表现不突出和纹理不清晰等问题,提出一种图像增强下使用生成对抗网络（GAN）和卷积神经网络（CNN）进行电子计算机断层扫描（CT）图像与磁共振成像（MRI）图像融合的方法。生成器针对高频特征图像,双鉴别器针对逆变换后的融合图像;高频特征图像通过GAN模型进行特征融合,低频特征图像通过基于迁移学习的CNN预训练模型进行特征融合。实验结果表明,与当前先进融合算法相比,所提方法在主观表现上纹理细节特征更加丰富,轮廓边缘信息更加清晰突出;在客观指标评估中,融合质量评价指标（QAB/F）、信息熵（IE）、空间频率（SF）、结构相似性（SSIM）、互信息（MI）和融合视觉信息保真度（VIFF）等关键指标比其他最佳测试结果分别提高了2.0%、6.3%、7.0%、5.5%、9.0%和3.3%。融合后图像可以有效地应用于医学诊断,进一步提高诊断效率。     

基于存储服务的JPEG2000医学DICOM图像压缩的研究

由于医学图像所包含的数据量巨大,不利于存储和在现有网络上有效地传输,本研究使用VC++实现基于联合图像专家组2000(Joint Photographic Experts Group2000,JPEG2000)图像压缩算法对医学图像进行处理,根据医学数字图像通讯(Digital Imaging and Communicationin Medicine,DICOM)标准中存储服务的规则实现医学图像的存储服务。研究结果表明,使用JPEG2000压缩算法处理后的医学图像,不仅能大大节约存储空间,而且能降低传输时所需要的带宽。因此,JPEG2000压缩算法在DICOM推广及远程医疗的发展中具有积极意义。     

乳腺肿瘤超声图像特征参数量化研究进展

乳腺肿瘤超声图像的特征量化分析对判别肿瘤的良、恶性具有重要价值。本文总结了良性和恶性乳腺肿瘤在超声图像上的特点，将乳腺良性肿瘤和恶性肿瘤鉴别特征在形状、边缘、边界、朝向、回声特点几个方面的量化方法和量化参数进行了较为全面的梳理，并对量化特征与肿瘤良、恶性之间的关系进行了分析。     

一种基于多尺度分析的CT图像边缘检测方法

为了准确提取CT图像中解剖组织几何形态特征,提出了一种基于多尺度分析的CT图像边缘检测方法。本文应用多尺度分析中含有尺度因子的平滑函数的负导数作为小波,对CT图像实施小波变换,并检测小波变换的模局部极大值,完成基于模局部极大值的解剖组织轮廓特征表达。本文还讨论了一种模局部极大值点的简单筛选方法,针对CT图像噪声较大的特点,以模局部极大值的均方根乘以一个与尺度有关的因子作为模局部极大值的阈值,在不同尺度上获得了清晰的边缘信息。阈值处理后的模局部极大值图表明,不同尺度下的边缘检测能给出大小不同的物体的边缘信息。本方法能在有效抑制噪声的基础上,准确提取感兴趣解剖组织的几何轮廓特征。     

DWT–DCT hybrid scheme for medical image compression

With the development of communication technology the applications and services of health telemetics are growing. In view of the increasingly important role played by digital medical imaging in modern health care, it is necessary for large amount of image data to be economically stored and/or transmitted. There is a need for the development of image compression systems that combine high compression ratio with preservation of critical information. During the past decade wavelets have been a significant development in the field of image compression. In this paper, a hybrid scheme using both discrete wavelet transform (DWT) and discrete cosine transform (DCT) for medical image compression is presented. DCT is applied to the DWT details, which generally have zero mean and small variance, thereby achieving better compression than obtained from either technique alone. The results of the hybrid scheme are compared with JPEG and set partitioning in hierarchical trees (SPIHT) coder and it is found that the performance of the proposed scheme is better.     

Medical image compression using DCT-based subband decomposition and modified SPIHT data organization

OBJECTIVE: The work proposed a novel bit-rate-reduced approach for reducing the memory required to store a remote diagnosis and rapidly transmission it. METHOD: In the work, an 8x8 Discrete Cosine Transform (DCT) approach is adopted to perform subband decomposition. Modified set partitioning in hierarchical trees (SPIHT) is then employed to organize data and entropy coding. The translation function can store the detailed characteristics of an image. A simple transformation to obtain DCT spectrum data in a single frequency domain decomposes the original signal into various frequency domains that can further compressed by wavelet-based algorithm. In this scheme, insignificant DCT coefficients that correspond to a particular spatial location in the high-frequency subbands can be employed to reduce redundancy by applying a proposed combined function in association with the modified SPIHT. RESULTS AND CONCLUSIONS: Simulation results showed that the embedded DCT-CSPIHT image compression reduced the computational complexity to only a quarter of the wavelet-based subband decomposition, and improved the quality of the reconstructed medical image as given by both the peak signal-to-noise ratio (PSNR) and the perceptual results over JPEG2000 and the original SPIHT at the same bit rate. Additionally, since 8x8 fast DCT hardware implementation being commercially available, the proposed DCT-CSPIHT can perform well in high speed image coding and transmission.     

医用内窥镜图像自适应量化压缩编化码方法研究

本文提出了一种与ＪＰＥＧ标准完全兼容的医用内窥镜图像自适应量化压缩编码方法,方法采用二次扫描的措施,根据原始图像的频谱分布特点,自适应地修正ＪＰＥＧ推荐的量化表。实验结果表明：该方法较之于标准ＪＰＥＧ图像压缩,峰值信噪比（ＰＳＮＲ）明显提高,可在相同压缩比下,保持更多的图像细节,特别适合于医学图像的压缩。     

Quality of Compressed Medical Images

Previous studies have shown that Joint Photographic Experts Group (JPEG) 2000 compression is better than JPEG at higher compression ratio levels. However, some findings revealed that this is not valid at lower levels. In this study, the qualities of compressed medical images in these ratio areas (∼20), including computed radiography, computed tomography head and body, mammographic, and magnetic resonance T1 and T2 images, were estimated using both a pixel-based (peak signal to noise ratio) and two 8 × 8 window-based [Q index and Moran peak ratio (MPR)] metrics. To diminish the effects of blocking artifacts from JPEG, jump windows were used in both window-based metrics. Comparing the image quality indices between jump and sliding windows, the results showed that blocking artifacts were produced from JPEG compression, even at low compression ratios. However, even after the blocking artifacts were omitted in JPEG compressed images, JPEG2000 outperformed JPEG at low compression levels. We found in this study that the image contrast and the average gray level play important roles in image compression and quality evaluation. There were drawbacks in all metrics that we used. In the future, the image gray level and contrast effect should be considered in developing new objective metrics.     

DWT-DCT hybrid scheme for medical image compression

With the development of communication technology the applications and services of health telemetics are growing. In view of the increasingly important role played by digital medical imaging in modern health care, it is necessary for large amount of image data to be economically stored and/or transmitted. There is a need for the development of image compression systems that combine high compression ratio with preservation of critical information. During the past decade wavelets have been a significant development in the field of image compression. In this paper, a hybrid scheme using both discrete wavelet transform (DWT) and discrete cosine transform (DCT) for medical image compression is presented. DCT is applied to the DWT details, which generally have zero mean and small variance, thereby achieving better compression than obtained from either technique alone. The results of the hybrid scheme are compared with JPEG and set partitioning in hierarchical trees (SPIHT) coder and it is found that the performance of the proposed scheme is better.     

JPEG2000有损压缩对核医学图像的压缩效果的客观定量研究

从线源图像获得线源扩展函数、调制转移函数和峰-总计数比等参数,定量研究JPEG 2000有损压缩对核医学图像的压缩效果,并与实际分辨率图像和模拟分辨率图像的压缩效果进行对比。实验发现,随压缩比的增加,峰-总计数比曲线逐步下降,且噪声水平越高,曲线下降越快;但调制转移函数曲线和线源扩展函数曲线的半高宽无明显变化;随压缩比的增加,分辨率图像上可辨认出的条纹逐渐减少。实验结果表明,在压缩比和噪声水平变化时,峰-总计数比的变化与图像质量的变化规律一致,是用于图像压缩效果评价的较好的客观定量参数。     

JPEG2000 Still Image Coding Quality

This work demonstrates the image qualities between two popular JPEG2000 programs. Two medical image compression algorithms are both coded using JPEG2000, but they are different regarding the interface, convenience, speed of computation, and their characteristic options influenced by the encoder, quantization, tiling, etc. The differences in image quality and compression ratio are also affected by the modality and compression algorithm implementation. Do they provide the same quality? The qualities of compressed medical images from two image compression programs named Apollo and JJ2000 were evaluated extensively using objective metrics. These algorithms were applied to three medical image modalities at various compression ratios ranging from 10:1 to 100:1. Following that, the quality of the reconstructed images was evaluated using five objective metrics. The Spearman rank correlation coefficients were measured under every metric in the two programs. We found that JJ2000 and Apollo exhibited indistinguishable image quality for all images evaluated using the above five metrics (r > 0.98, p < 0.001). It can be concluded that the image quality of the JJ2000 and Apollo algorithms is statistically equivalent for medical image compression.     

Adaptive threshold-based block classification in medical image compression for teleradiology

Telemedicine, among other things, involves storage and transmission of medical images, popularly known as teleradiology. Due to constraints on bandwidth and storage capacity, a medical image may be needed to be compressed before transmission/storage. Among various compression techniques, transform-based techniques that convert an image in spatial domain into the data in spectral domain are very effective. Discrete cosine transform (DCT) is possibly the most popular transform used in compression of images in standards like Joint Photographic Experts Group (JPEG). In DCT-based compression the image is split into smaller blocks for computational simplicity. The blocks are classified on the basis of information content to maximize compression ratio without sacrificing diagnostic information. The present paper presents a technique along with computational algorithm for classification of blocks on the basis of an adaptive threshold value of variance. The adaptive approach makes the classification technique applicable across the board to all medical images. Its efficacy is demonstrated by applying it to CT, X-ray and ultrasound images and by comparing the results against the JPEG in terms of various objective quality indices.     

MRI-PET image fusion based on NSCT transform using local energy and local variance fusion rules

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to evaluate the fusion result. In visual evaluation the opinion of two radiologists was used and in quantitative evaluation the proposed fusion method was compared with six existing methods and used criteria were entropy, mutual information, discrepancy and overall performance.     

The impact of image information on compressibility and degradation in medical image compression

The aim of the study was to demonstrate and critically discuss the influence of image information on compressibility and image degradation. The influence of image information on image compression was demonstrated on the axial computed tomography images of a head. The standard Joint Photographic Expert Group (JPEG) and JPEG 2000 compression methods were used in compression ratio (CR) and in quality factor (QF) compression modes. Image information was estimated by calculating image entropy, while the effects of image compression were evaluated quantitatively, by file size reduction and by local and global mean square error (MSE), and qualitatively, by visual perception of distortion in high and low contrast test patterns. In QF compression mode, a strong correlation between image entropy and file size was found for JPEG (r=0.87, p < 0.001) and JPEG 2000 (r=0.84, p < 0.001), while corresponding local MSE was constant (4.54) or nearly constant (2.36-2.37), respectively. For JPEG 2000 CR compression mode, CR was nearly constant (1:25), while local MSE varied considerably (2.26 and 10.09). The obtained qualitative and quantitative results clearly demonstrate that image degradation highly depends on image information, which indicates that the degree of image degradation cannot be guaranteed in CR but only in QF compression mode. CR is therefore not a measure of choice for expressing the degree of image degradation in medical image compression. Moreover, even when using QF compression modes, objective evaluation, and comparison of the compression methods within and between studies is often not possible due to the lack of standardization of compression quality scales.