MEDICAL IMAGE COMPRESSION BY VECTOR QUANTIZATION

通过矢量量化进行医学图像压缩

• 批准号: 3193941
• 负责人: ROBERT M GRAY
• 金额: $ 25.11万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1990
• 资助国家: 美国
• 起止时间: 1990-08-01 至 1993-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3193941
• 关键词: computed axial tomography; computer assisted diagnosis; computer assisted medical decision making; computer graphics /printing; diagnosis quality /standard; image processing; magnetic resonance imaging; mathematical model

Current and anticipated rates of acquisition of digital medical images threaten to overwhelm the available capacities for digital storage media and digital communication links, even with the arrival of optical disk storage and optical fiber communication. This necessitates efficient coding and compression of images for both archives and communication. The goal of the proposed research is to extend existing techniques and develop new techniques for image coding and compression well matched to medical image applications, The propose algorithms combine recent advances from vector quantization (vector coding, block quantization) and decision tree design and combine aspects of compression with low-level classification so as to permit the best (or fastest) reproduction in areas of an image of most interest to the user. The research will be conducted by specialist in coding and signal processing in cooperation with radiologists. The specific aims of the research are: 1. To develop algorithms for compressing medical images to one bit per pixel (picture element) and less with little or no loss in perceived image quality or diagnostic accuracy using vector quantization, a form of lossy data compression that optimizes fidelity for a given bit rate in bits per pixel. This will provide compression ratios of at least 8:1 for typical 8 bit per pixel monochrome images. We believe good quality images will be feasible at rates of 1/2 bit per pixel (16:1) and less. The algorithms foe encoding and decoding should be implementable in real time using current technology. Specific algorithms to be considered include tree- structured vector quantizers and recursive vector quantizers (including predictive and finite-state codes). Special emphasis will be given to progressive transmission applications where quality improves as additional bits arrive from storage or communication media. 2. To evaluate clinically the quality of the compressed images at various compression ratios. Image evaluation will be done by diagnostic radiologists in experiments designed in conjunction with biostatisticians to judge quantitatively and qualitatively the coded images both for the detection of important features and for the preservation of selected measurements. The primary applications considered will be computes tomography (CT) and magnetic resonance (MR) images (including CINE mode MR images consisting of sequences of MR heart images).

当前和预期的数字医学图像获取率 威胁要淹没数字存储媒体的可用能力 和数字通信链接，即使光盘的到来 存储和光纤通信。 这需要有效 档案和通信的图像编码和压缩。 这 拟议研究的目标是扩展现有技术和 开发图像编码和压缩的新技术非常匹配 医疗图像应用，提出的算法结合了最近 矢量量化的进步（向量编码，块量化）和 决策树设计并将压缩的各个方面与低级 分类以允许区域最好（或最快）的繁殖 用户最感兴趣的图像。 研究将进行 由专家与合作进行编码和信号处理 放射科医生。 研究的具体目的是： 1。开发用于压缩医学图像至一位的算法 像素（图片元素），而感知图像中几乎没有损失或没有损失 使用矢量量化的质量或诊断精度，一种有损的形式 数据压缩，可优化给定比特率的保真度。 像素。 这将提供典型8的压缩比至少为8：1 每个像素单色图像。 我们相信高质量的图像将是 每像素（16：1）以1/2位的速度可行。 算法 敌人编码和解码应可以实时实现 当前技术。 要考虑的特定算法包括树 - 结构化矢量量化器和递归矢量量化器（包括 预测性和有限状态代码）。 特别强调 随着质量提高的渐进传输应用 位来自存储或通信媒体。 2。在临床上评估各种压缩图像的质量 压缩比。 图像评估将通过诊断进行 放射科医生与生物统计学家一起设计的实验中 定量和定性地对编码图像进行判断 检测重要特征并保存选定的特征 测量。 考虑的主要应用程序将是计算 断层扫描（CT）和磁共振（MR）图像（包括Cine Mode MR 由MR心脏图像序列组成的图像）​​。