Optimized Compression for Telepathology Virtual Slides

远程病理学虚拟载玻片的优化压缩

• 批准号: 7499543
• 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
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-21 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7499543
• 关键词: Accounting; Achievement; Affect; Area; Arizona; Arts; Bite; Breast; Collaborations; Color; Core Biopsy; Data; Data Compression; Detection; Diagnosis; Diagnostic; Discrimination; Ensure; Eye; Goals; Human; Human Resources; Image; Image Compression; Individual; Investigation; Lesion; Measures; Medical Imaging; Methods; Metric; Modeling; Morphologic artifacts; Numbers; Pathology; Patients; Performance; Positioning Attribute; Property; Quality Control; Rate; Receiver Operating Characteristics; Research; Scanning; Schedule; Scheme; Slide; Surgical Pathology; Techniques; Technology; Telemedicine; Telepathology; Testing; Time; Universities; Vision; Visual; Visual system structure; base; diagnostic accuracy; digital; image reconstruction; improved; interest; programs; research study; sensor; simulation; transmission process; virtual; visual search

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)：我们建议研究一种方法，通过使用基于人类视觉系统的特性和能力的信息的图像压缩方案来改进远程病理图像(即虚拟幻灯片)的呈现，以用于准确的诊断。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，这些研究将压缩方案、显示器和临床医生作为一个动态和集成的实体，而不是相互独立操作的单个部分。我们相信，通过这种方法，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)一致一致的视觉质量，通过优化图像、患者和显示技术的可变压缩率；以及(3)基于对人类观察者的模拟进行自动、客观的图像质量控制。我们最重要的假设是，通过根据有关人类视觉系统的能力和限制的信息定制图像压缩方案和显示，有可能改善远程病理图像的呈现，以实现准确的诊断。一旦我们确认了这种方法对远程病理图像的实用性，它就可以很容易地推广到使用数字彩色图像进行存储转发传输的其他远程医疗应用，例如远程皮肤病，以及实时视频远程医疗应用(例如，远程医疗)。我们假设，使用基于关于人类视觉系统的能力和限制的信息的图像压缩方案，有可能改善远程病理图像的呈现，以便进行准确的诊断。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展示视觉辨别模型(VDM)在各种存储转发和实时视频远程医疗应用中作为压缩函数来预测观察者性能(即，检测和/或辨别医学图像中的病变)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它们被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(评估诊断性能的一般首选方法)可以被避免或在范围上显著减少，同时仍可评估给定压缩方案或压缩水平对诊断性能的影响。