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Optimized Compression for Telepathology Virtual Slides

远程病理学虚拟载玻片的优化压缩

基本信息

批准号：
7676727
负责人：
Elizabeth A Krupinski
金额：
$ 40.13万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-09-21 至 2011-08-31
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): We propose to investigate a method to improve the presentation of telepathology images (i.e., virtual slides) for accurate diagnoses by using image compression schemes based on information about the properties and capabilities of the human visual system. Although image compression techniques have been studied and evaluated for use in many telepathology applications, we believe that there has yet to be a thorough investigation in this area that combines state-of-the-art compression methods with vision modeling and observer performance studies that take into account the compression schemes, the display, and the clinician as a dynamic and integrated entity rather than individual parts that operate independently of one another. Through this approach we believe that we can tailor image compression schemes for specific telepathology applications thereby enabling: (1) faster delivery of images without compromising visual quality, utility or diagnostic accuracy; (2) consistently uniform visual quality with variable compression rates optimized across images, patients, and display technologies; and (3) automatic, objective image-quality control based on simulations of the human observer. Our overriding hypothesis is that it is possible to improve the presentation of telepathology images for accurate diagnoses by tailoring image compression schemes and displays that are based on information about the capabilities and limitations of the human visual system. Once we have confirmed the utility of this approach with telepathology images, it can easily be generalized to other telemedicine applications that use digital color images for store-and-forward transmission such as teledermatology, as well as real-time video telemedicine applications (e.g., telepsychiatry). We hypothesize that it is possible to improve the presentation of telepathology images for accurate diagnoses using image compression schemes that are based on information about the capabilities and limitations of the human visual system. We propose two specific aims to test this hypothesis. An underlying long-term goal is to demonstrate the utility of a visual discrimination model (VDM) for predicting observer performance (i.e., detection and/or discrimination of lesions in medical images) as a function of compression in a variety of both store-forward and real-time video telemedical applications. The achievement of this goal will ensure that as more compression schemes are developed or as they are applied to unique imaging applications, time and personnel intensive Receiver Operating Characteristic (ROC) experiments (the generally preferred method for evaluating diagnostic performance) can either be avoided or reduced significantly in scope while still assessing the effects of a given compression scheme or level of compression on diagnostic performance.

描述（由申请人提供）：我们提出研究一种改进远程病理学图像的呈现的方法（即，虚拟幻灯片），以便通过使用基于关于人类视觉系统的特性和能力的信息的图像压缩方案来进行准确诊断。虽然图像压缩技术已经被研究和评估用于许多远程病理学应用，但我们认为，在这一领域还没有一个彻底的调查，将最先进的压缩方法与视觉建模和观察者性能研究相结合，考虑到压缩方案，显示，并且临床医生作为动态的和集成的实体而不是彼此独立操作的单独部分。通过这种方法，我们相信我们可以为特定的远程病理学应用定制图像压缩方案，从而实现：（1）在不影响视觉质量、实用性或诊断准确性的情况下更快地提供图像;（2）在图像、患者和显示技术之间优化可变压缩率的情况下，具有一致的统一视觉质量;以及（3）基于人类观察者的模拟的自动、客观的图像质量控制。我们最重要的假设是，它是可能的，以提高远程病理图像的准确诊断的介绍，通过定制的图像压缩方案和显示，是基于人类视觉系统的能力和局限性的信息。一旦我们已经证实了这种方法对远程病理学图像的效用，它就可以很容易地推广到使用数字彩色图像进行存储和转发传输的其他远程医疗应用，例如远程皮肤病学，以及实时视频远程医疗应用（例如，telepsychiatry）。我们假设，这是可能的，以提高远程病理图像的准确诊断使用图像压缩方案，是基于人类视觉系统的能力和局限性的信息的介绍。我们提出了两个具体的目标来检验这一假设。一个潜在的长期目标是证明视觉辨别模型（VDM）用于预测观察者表现（即，检测和/或区分医学图像中的损伤）作为压缩的函数，在各种存储转发和实时视频远程医疗应用中。这一目标的实现将确保随着更多的压缩方案被开发或当它们被应用于独特的成像应用时，时间和人员密集的受试者工作特性（ROC）实验（用于评估诊断性能的通常优选的方法）可以被避免或在范围上显著减少，同时仍然评估给定的压缩方案或压缩水平对诊断性能的影响。