Optimized Compression for Telepathology Virtual Slides

描述（由申请人提供）：我们建议研究一种方法，通过使用基于人类视觉系统属性和能力信息的图像压缩方案来改善心灵病理学图像（即虚拟幻灯片）的呈现，以便准确诊断。尽管图像压缩技术已经被研究和评估用于许多心灵病理学应用，但我们认为，在这一领域还需要进行彻底的调查，将最先进的压缩方法与视觉建模和观察者性能研究结合起来，将压缩方案、显示和临床医生作为一个动态的、综合的实体，而不是相互独立操作的单个部分。通过这种方法，我们相信我们可以为特定的心灵病理学应用量身定制图像压缩方案，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交付图像；(2)通过优化不同图像、患者和显示技术的可变压缩率，保持一致的视觉质量；(3)基于人类观察者模拟的自动、客观的图像质量控制。我们最重要的假设是，通过定制基于人类视觉系统能力和局限性信息的图像压缩方案和显示，有可能改善心灵病理学图像的准确诊断。一旦我们确认了这种方法在远程病理学图像中的实用性，它就可以很容易地推广到其他使用数字彩色图像进行存储和转发传输的远程医疗应用中，例如远程皮肤病学，以及实时视频远程医疗应用（例如远程精神病学）。我们假设，使用基于人类视觉系统的能力和局限性信息的图像压缩方案，有可能改善心灵病理学图像的准确诊断。我们提出两个具体目标来检验这一假设。潜在的长期目标是证明视觉识别模型（VDM）在预测观察者性能（即，检测和/或区分医学图像中的病变）作为压缩功能在各种存储前向和实时视频远程医疗应用中的实用性。这一目标的实现将确保随着更多压缩方案的开发或应用于独特的成像应用，时间和人员密集的接收器工作特性（ROC）实验（通常首选的评估诊断性能的方法）可以避免或显着减少范围，同时仍然评估给定压缩方案或压缩水平对诊断性能的影响。

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.