Algorithms and Image Analysis Software Tool for Automated Recognition and Identif

用于自动识别和识别的算法和图像分析软件工具

• 批准号: 7901383
• 负责人: JELENA KOVACEVIC
• 金额: $ 7.03万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7901383
• 关键词: Algorithms; Area; Automation; Basic Science; Biological; Biology; Cells; Classification; Communities; Computer Systems; Computer software; Data; Data Set; Development; Diagnosis; Disease; Drosophila genus; Embryo; Family; Figs - dietary; Flavoring; Frequencies; Generic Drugs; Germ Layers; Goals; Hand; Histocompatibility Testing; Histology; Image; Image Analysis; Label; Location; Medical; Medicine; Methods; Mining; Molecular; Motivation; Organ; Otitis Media; Pathologic Processes; Pattern; Process; Proteins; Research; Resources; Role; Software Tools; Specificity; Staging; System; Teratoma; Testing; Tissues; Training; Trees; Voting; Weight; Work; base; data mining; flexibility; image processing; innovation; novel; open source; public health relevance; success; tool; user-friendly; wasting

DESCRIPTION (provided by applicant): In recent years, biologists and clinicians have gained access to unprecedented amounts of imaging data, depicting static and dynamic processes in cells and tissues. While this trove hides answers to a host of important questions, mining it visually, as it is typically done, is an enormous and error-prone task wasting valuable resources. As such, the automation of this processing has become an important area of emerging research. Classification, a standard task in image processing, underlies many problems in medicine and biology, such as recognizing proteins based on their subcellular location patterns, determination of developmental stages in Drosophila embryos, recognizing tissues in histology and diagnosis of otitis media. Thus: We propose to develop a flexible, modular and accurate algorithm and a software toolbox to automatically recognize and identify normal and pathological processes occurring in disease and development. A generic classification system computes a set of numerical features describing the data, followed by separating these features into classes. We propose to decompose the image first using a multiresolution transform, as we postulate that multiresolution subspaces hide valuable information. Each subspace performs separate classification, giving its vote. The arbiter reconciling these local votes into a single, global one, is the weighting block. It assigns a weight to each subspace based on how reliable its voting has been during training. Based on our preliminary work, we believe this system to have great potential for accurate and robust classification (recognition, identification) of normal and pathological processes occurring in disease and development. Specific Aim 1: Develop a classification algorithm based on multiresolution transforms, that is flexible, modular and accurate, and has an efficient implementation. Specific Aim 2: Develop a flexible classification software platform and a user-friendly GUI to facilitate both use by biologists and clinicians, as well as their interaction with algorithm developers. Significance of the Proposed Work: The flexibility and modularity of the proposed system together with features developed for our three testbeds will allow for a broad use in a wide range of applications within the broad hierarchy of organ development. The distribution of the software as an open-source ImageJ plugin will allow for its wide use in the biological and medical communities. Innovation the Proposed Work Brings. The algorithm we propose is flexible, accurate and novel: multiresolution tools offer a window into previously unseen features within a dataset. Each block of the multiresolution classifier will offer a novel contribution: (1) construction of frame families in the multiresolution block, (2) novel features in the feature extractor block, (3) multiresolution versions of known classifiers in the classifier block. Moreover, the testbeds we consider do not have an available tool for automated classification. PUBLIC HEALTH RELEVANCE: Narrative The motivation is for this algorithm and software toolbox to be available to the biological and medical communities for mining imaging data. As our three testbeds span various scales within the broad hierarchy of organ development, the success of our system will bring advances both in basic research at molecular and cellular levels (Drosophila project) as well as at tissue and organ levels (histology and otitis media projects).

描述（由申请人提供）：近年来，生物学家和临床医生已经获得了前所未有的成像数据，描绘了细胞和组织中的静态和动态过程。虽然这trove掩盖了许多重要问题的答案，但通常以直观的方式挖掘它是一项巨大而容易出错的任务，浪费了宝贵的资源。因此，这种处理的自动化已成为新兴研究的重要领域。分类是图像处理中的标准任务，是医学和生物学领域的许多问题，例如基于其亚细胞位置模式识别蛋白质，确定果蝇胚胎中发育阶段的确定，识别组织学中的组织学和耳炎培养基的诊断。因此：我们建议开发一种灵活，模块化和准确的算法和软件工具箱，以自动识别和识别疾病和发育中发生的正常和病理过程。通用分类系统计算一组描述数据的数值功能，然后将这些功能分为类。我们建议首先使用多分辨率变换分解图像，因为我们假设多分辨率子空间隐藏了有价值的信息。每个子空间都执行单独的分类，进行投票。仲裁者将这些本地投票汇总为一个全球的投票是加权块。它根据其在培训期间的投票方式可靠地分配了每个子空间的权重。基于我们的初步工作，我们认为该系统具有巨大的潜力，可以对疾病和发育中发生的正常和病理过程进行准确和稳健的分类（识别，识别）。特定目的1：基于灵活，模块化和准确的多分辨率变换的分类算法，并具有有效的实现。特定目标2：开发灵活的分类软件平台和用户友好的GUI，以促进生物学家和临床医生的使用以及与算法开发人员的互动。拟议工作的重要性：拟议系统的灵活性和模块化以及为我们的三个测试床开发的功能将允许在器官开发的广泛层次结构中广泛使用。该软件作为开源ImageJ插件的分布将允许在生物和医疗社区中广泛使用。创新拟议的工作带来了。我们提出的算法是灵活，准确和新颖的：多解决工具为数据集中的以前看不见的功能提供了一个窗口。多分辨率分类器的每个块将提供一个新颖的贡献：（1）在多分辨率块中构造框架家族，（2）特征提取器块中的新特征，（3）分类器块中已知分类器的多分辨率版本。此外，我们认为的测试台没有可用的自动分类工具。公共卫生相关性：叙事动机是针对该算法和软件工具箱，可供生物学和医疗社区用于采矿成像数据。随着我们的三个测试床在器官发育的广泛层次结构中遍及各种规模，我们系统的成功将在分子和细胞水平（果蝇项目）以及组织和器官水平（组织学和耳炎媒体项目）的基础研究中取得进步。

项目成果

A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images.

• DOI: 10.1117/12.893641
• 发表时间: 2011-10-19
• 期刊: Proceedings of SPIE--the International Society for Optical Engineering
• 作者: Massar ML;Bhagavatula R;Ozolek JA;Castro CA;Fickus M;Kovačević J
• 通讯作者: Kovačević J

AUTOMATIC IDENTIFICATION AND DELINEATION OF GERM LAYER COMPONENTS IN H&E STAINED IMAGES OF TERATOMAS DERIVED FROM HUMAN AND NONHUMAN PRIMATE EMBRYONIC STEM CELLS.

• DOI: 10.1109/isbi.2010.5490168
• 发表时间: 2010-04-14
• 期刊: Proceedings. IEEE International Symposium on Biomedical Imaging
• 作者: Bhagavatula R;Fickus M;Kelly W;Guo C;Ozolek JA;Castro CA;Kovačević J
• 通讯作者: Kovačević J

AUTOMATED COLITIS DETECTION FROM ENDOSCOPIC BIOPSIES AS A TISSUE SCREENING TOOL IN DIAGNOSTIC PATHOLOGY.

内窥镜活检中的自动结肠炎检测作为诊断病理学中的组织筛查工具。

• DOI: 10.1109/icip.2012.6467483
• 发表时间: 2012
• 期刊: Proceedings. International Conference on Image Processing
• 作者: McCann,MichaelT;Bhagavatula,Ramamurthy;Fickus,MatthewC;Ozolek,JohnA;Kovačević,Jelena
• 通讯作者: Kovačević,Jelena

Systematic Construction of Real Lapped Tight Frame Transforms.

真实搭接紧框架变换的系统构建。

• DOI: 10.1109/tsp.2010.2041865
• 发表时间: 2010
• 期刊: IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society
• 作者: Sandryhaila,Aliaksei;Chebira,Amina;Milo,Christina;Kovčcević,Jelena;Püschel,Markus
• 通讯作者: Püschel,Markus