一种基于提取轮廓的医学图象数据压缩编码方法

本文提出了一种对医学图象进行轮廓编码和采用二维快速离散余弦变换（2D－FDCT）对其背景图象数据压缩编码相结合的方法。首先对2^t*2^t（t=9)，点的原始医学数字图象采用可变阈值的Sobel算子提出其边缘图象，原始图象与边缘图象之差为背景图象，利用5＊5点邻域窗函数对此背景图象2：1内抽成2t^-1*2^t-1点的数字图象，利用上述方法对此2t-1*2t-1点的数字图象进行分解……直到剩下64＊64点的背景图象，本文对几级轮廓图象采用等值线编码压缩，对64＊64点的背景图象通过采用快速多项式变换（FPT）计算2D－FDCT来进行数据压缩编码，在接收端，在接收端，对恢复的各级背景图象再叠加上相应的[边缘图象可减少重建图象产生的边缘模糊效应，我们在IBM－PC／XT微机组成的小型图象处理系统上进行了医学的编码压缩实验，图象数据压缩比可达20：1以上。     

一种基于人类感知的医学图象压缩方法

作者详细地介绍了一种基于人类感知的医学图象压缩算法，它利用人类视觉的运动特性，空间频率特性及时间频率特性对静止灰度图象进行有限失真压缩，该方法能大大压缩图象数据提高压缩比，对医学图象的压缩是一种比较有效的方法。     

基于描述的微血管网络图象压缩

微血管网络图象已日渐成为临床诊断和科学研究的重要依据，为了压缩无用数据，保留已有测量结果，本文提出了基于描述的微血管网络图象压缩技术。通过血管提取、细化等步骤，最后建立了相应于原血管图的数学描述结构，这种方法使数据达到了极高的压缩比。借助于生成的描述结构，可以方便地获得已有的测量结果而无需对图象作恢复和再测量。由于描述保留了足够充分的网络构造信息，因此使网络重构成为可能。     

三维医学图象中的表面提取与显示

本文介绍一种对由序列断层重建的三维图象进行表面提取、表示和显示的方法,将原始图象用一种K叉树数据结构表示,通过两个局部算子的走树过程消去非表面点,从而提取某个特定物体的表面并将其表示为K叉树。这种结构有效地压缩表面数据,也简化了显示过程中的变换运算。     

基于二维DCT的医学图象压缩及FPGA实现

本文介绍了一种适用于JPEG医学图象压缩的二维DCT的FPGA设计方案。该设计充分利用了可编程逻辑器件的灵活性以及器件本身所带的硬件资源，如嵌入式乘法器。二维离散余弦变换采用了行列分解的方法，并通过快速算法在很大程度上减少了硬件实现的复杂度，提高了模块的吞吐量，并且具有实时，高精度的优点。     

医学图象的边界处理

医学图象质量的好坏直接影响医务人员对疾病的诊断与治疗。由于有时候医学图象的边界不清晰，甚至很模糊，人眼难以判断，因此需要一种特殊的算法来实现边界点的检测，边界点检测的方法很多，作者讨论用微分法借助于计算机来处理医学图象的边界问题。利用微分运算获得的梯度算子。根据区域边界处灰度呈现不连续的特点，通过对图象函数ｆ（ｘ，ｒ）（灰度特性函数）进行必要的运算，来达到检测边界元素的目的，进而实现边界点的连接，     

超声医学图象的计算机辅助解释

本文提出一种用于超声医学图象自动解释的新方法。该方法是在图象预分割的基础上,将区域生长算法、目标检测和基于知识的解释综合为双向驱动推理过程,从而完成对图象中器官、组织的识别和解释。作者已应用该方法对特定类超声医学图象进行了计算机分析实验,获得了较为理想的实验结果,并在文中对实验结果加以讨论。这一研究工作为建造医学图象自动解释系统提供了一个有较的方法。     

线性的医学图像边界检测方法中六种算子的比较

图象的边界检测是图象处理的一个重要部分。本文根据实践经验首先提出了边界检测的评判标准，继而对几种线性算法进行了描述，并将它们用ＶｉｓｕａｌＢａｓｉｃ语言对同一图象进行了实现，从而直观地反映了各算法的优劣，为我们处理实际的医学图象提供了基础。     

三维医学图象及其应用

三维医学图象的特点及其图象获取图象作为诊断和治疗规划的工具,在现代医学临床中已必不可少。从X线透视到核磁共振层析图象(MRI),为医生提供了各种不同的诊断信息。X线透视、X-CT、超声图象等人体组织器官的形态信息,而核医学图象(包括单光子发射式CT和正子发射式CT)以及核磁共振图象不但提供人体组织器官的形态信息,而且还可以提供有关化学元素的分布及其生物功能活动信息。     

医学断层图象的立体切割算法

采用计算机图形学和图象处理技术对图像三维信息的提取和三维显示可以帮助医务人员模拟治疗和技术过程，提高医疗诊断的准确性和科学性。立体图像切割作为三维图象显示的一种有用的方式，可以提供剖面信息，便于诊断。我们提出了一种在断层图象与剖面相交后的轮廓计算以及剖面图象插值的方法，结果表明，可以得到很好的切割效果。     

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。     

基于最佳截断嵌入码块编码和离散小波变换的医学图像复合压缩算法

根据医学图像信息相对集中的特点,提出了一种基于最佳截断嵌入码块编码和离散小波变换的医学图像任意形状感兴趣区域复合压缩方法,通过对图像感兴趣区域和背景区采用不同的编码方式,提高了医学图像压缩比,并确保了医学图像感兴趣区域的高质量重建。实验表明:该方法在重建图像质量和压缩比方面均达到了较好的性能。     

Projection-Based Medical Image Compression for Telemedicine Applications

Recent years have seen great development in the field of medical imaging and telemedicine. Despite the developments in storage and communication technologies, compression of medical data remains challenging. This paper proposes an efficient medical image compression method for telemedicine. The proposed method takes advantage of Radon transform whose basis functions are effective in representing the directional information. The periodic re-ordering of the elements of Radon projections requires minimal interpolation and preserves all of the original image pixel intensities. The dimension-reducing property allows the conversion of 2D processing task to a set of simple 1D task independently on each of the projections. The resultant Radon coefficients are then encoded using set partitioning in hierarchical trees (SPIHT) encoder. Experimental results obtained on a set of medical images demonstrate that the proposed method provides competing performance compared with conventional and state-of-the art compression methods in terms of compression ratio, peak signal-to-noise ratio (PSNR), and computational time.     

医学图像压缩技术研究进展

随着医学成像方式的不断增加,目前迫切需要解决庞大的图像数据的存储和传输的问题,压缩成为解决该问题不可或缺的重要方法之一.对目前医学图像压缩领域的技术进行了总结和分析,详细介绍了基于感兴趣区域的压缩、无损压缩、小波变换和基于神经网络的压缩方法,并对压缩算法的评估给出了几点标准,最后对医学图像压缩领域的发展前景提出了一些看法.     

基于提升小波变换和SPIHT的医学图像编码算法

目的:探讨一种基于提升小波变换和多级树集合分裂算法(set partitioning in hierarchical trees,SPIHT)的医学图像编码算法。方法:针对传统小波浮点数运算,计算量大的缺点,采用提升格式小波,结合多级树集合分裂算法和算术编码,实现对医学图像的编码。结果:在获得较高压缩比的情况下,能保证医学图像的重建质量,满足医学图像数据的存储和传输的需要。结论:仿真结果表明在相同压缩比的情况下,重建图像的峰值信噪比有明显提高,获得了较好的压缩效果。     

Optimization of block size for DCT-based medical image compression

In view of the increasing importance of medical imaging in healthcare and the large amount of image data to be transmitted/stored, the need for development of an efficient medical image compression method, which would preserve the critical diagnostic information at higher compression, is growing. Discrete cosine transform (DCT) is a popular transform used in many practical image/video compression systems because of its high compression performance and good computational efficiency. As the computational burden of full frame DCT would be heavy, the image is usually divided into non-overlapping sub-images, or blocks, for processing. This paper aims to identify the optimum size of the block, in reference to compression of CT, ultrasound and X-ray images. Three conflicting requirements are considered, namely processing time, compression ratio and the quality of the reconstructed image. The quantitative comparison of various block sizes has been carried out on the basis of benefit-to-cost ratio (BCR) and reconstruction quality score (RQS). Experimental results are presented that verify the optimality of the 16 × 16 block size.     

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.     

Novel MOS prediction models for compressed medical image quality

This paper presents the development of novel models which can be potentially useful in determining the upper limit of image compression thresholds, to preserve diagnostically relevant information in compressed medical images. These models were developed by evolving the correlation between the theoretically computed objective (peak signal-to-noise ratio and structural similarity) and subjective mean opinion score (MOS) quality parameters. The developed models were validated by comparing the model generated MOS with the corresponding experimental MOS of six independent observers considering joint photographic experts group (JPEG), JPEG2000 and set partitioning in hierarchical trees (SPIHT) compressions of computed tomography (CT) scan images. It is found that the correlation between the model generated and experimental MOS and PRD are ≥0.87 and ≤13% respectively for the compression range 0.05–2.0 bits/pixel of the CT scan images. Therefore our models can be potentially useful for observer-independent MOS prediction and quality assessment of reconstructed medical images. In addition this also avoids the need for exhaustive and time-consuming experimental MOS and thus it can be more suitable for teleradiology applications.     

The Application Research of a Fast Recursive Predictive Algorithm on Medical X-ray Image Compression

GENERAL　　As the rapid developmentof modern medical imaging technology and image pro-cessing technology,the traditional method of image grabbing and archiving based onfilm will be replaced by medical digital imaging technology forinstance CT and MRI.Specially,the rapid development of remote medical consultation and the appearanceof PACS system have already proved thatthe non- film medical digital imaging tech-nologies have life- force.Because of the large amount data of medical image,it…     

Medical image compression based on a morphological representation of wavelet coefficients.

Image compression is fundamental to the efficient and cost-effective use of digital medical imaging technology and applications. Wavelet transform techniques currently provide the most promising approach to high-quality image compression which is essential for diagnostic medical applications. A novel approach to image compression based on the wavelet decomposition has been developed which utilizes the shape or morphology of wavelet transform coefficients in the wavelet domain to isolate and retain significant coefficients corresponding to image structure and features. The remaining coefficients are further compressed using a combination of run-length and Huffman coding. The technique has been implemented and applied to full 16 bit medical image data for a range of compression ratios. Objective peak signal-to-noise ratio performance of the compression technique was analyzed. Results indicate that good reconstructed image quality can be achieved at compression ratios of up to 15:1 for the image types studied. This technique represents an effective approach to the compression of diagnostic medical images and is worthy of further, more thorough, evaluation of diagnostic quality and accuracy in a clinical setting.