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，通过使用基于有关人类视觉系统的属性和功能的信息的图像压缩方案来改进远程病理学图像（即虚拟幻灯片）的呈现，以进行准确的诊断。尽管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，该领域尚未进行彻底的研究，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、显示器和临床医生视为一个动态且集成的实体，而不是彼此独立运行的各个部分。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）更快地交付图像，而不会影响视觉质量、实用性或诊断准确性； (2) 始终一致的视觉质量，以及跨图像、患者和显示技术优化的可变压缩率； (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，通过根据有关人类视觉系统的功能和限制的信息定制图像压缩方案和显示，可以改进远程病理学图像的呈现以进行准确的诊断。一旦我们通过远程病理学图像确认了这种方法的实用性，就可以轻松地将其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程序，例如远程皮肤病学以及实时视频远程医疗应用程序（例如，远程精神病学）。我们假设，可以使用基于有关人类视觉系统的功能和限制的信息的图像压缩方案来改进远程病理学图像的呈现，以进行准确的诊断。我们提出了两个具体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）在预测观察者性能（即医学图像中病变的检测和/或辨别）方面的实用性，作为各种存储转发中的压缩函数和实时视频远程医疗应用。这一目标的实现将确保随着更多压缩方案的开发或将其应用于独特的成像应用，可以避免或避免时间和人员密集的接收器操作特性 (ROC) 实验（评估诊断性能的通常首选方法）。范围显着缩小，同时仍然评估给定压缩方案或压缩级别对诊断性能的影响。