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
基本信息
- 批准号:7901383
- 负责人:
- 金额:$ 7.03万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AlgorithmsAreaAutomationBasic ScienceBiologicalBiologyCellsClassificationCommunitiesComputer SystemsComputer softwareDataData SetDevelopmentDiagnosisDiseaseDrosophila genusEmbryoFamilyFigs - dietaryFlavoringFrequenciesGeneric DrugsGerm LayersGoalsHandHistocompatibility TestingHistologyImageImage AnalysisLabelLocationMedicalMedicineMethodsMiningMolecularMotivationOrganOtitis MediaPathologic ProcessesPatternProcessProteinsResearchResourcesRoleSoftware ToolsSpecificityStagingSystemTeratomaTestingTissuesTrainingTreesVotingWeightWorkbasedata miningflexibilityimage processinginnovationnovelopen sourcepublic health relevancesuccesstooluser-friendlywasting
项目摘要
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获得了前所未有的大量成像数据,描绘了细胞和组织中的静态和动态过程。虽然这个宝库隐藏了许多重要问题的答案,但像通常所做的那样,以视觉方式挖掘它是一项巨大且容易出错的任务,浪费宝贵的资源。因此,该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一项标准任务,是医学和生物学中许多问题的基础,例如根据亚细胞定位模式识别蛋白质、确定果蝇胚胎的发育阶段、识别组织学中的组织和诊断中耳炎。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展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字特征,然后将这些特征分成几类。我们建议首先使用多分辨率变换来分解图像,因为我们假设多分辨率子空间隐藏了有价值的信息。每个子空间执行单独的分类,并进行投票。仲裁者将这些本地投票调整为单一的全球投票,这就是加权块。它根据训练期间投票的可靠性为每个子空间分配权重。根据我们的初步工作,我们相信该系统在对疾病和发展中发生的正常和病理过程进行准确和稳健的分类(识别、鉴定)方面具有巨大潜力。具体目标1:开发一种基于多分辨率变换的分类算法,该算法灵活、模块化、准确,并且具有高效的实现能力。具体目标 2:开发灵活的分类软件平台和用户友好的 GUI,以方便生物学家和临床医生的使用以及他们与算法开发人员的交互。拟议工作的意义:拟议系统的灵活性和模块化以及为我们的三个测试平台开发的功能将允许在器官发育的广泛层次中广泛应用。该软件作为开源 ImageJ 插件的分发将使其在生物和医学界得到广泛使用。拟议工作带来的创新。我们提出的算法灵活、准确且新颖:多分辨率工具为了解数据集中以前未见过的特征提供了一个窗口。多分辨率分类器的每个块将提供新颖的贡献:(1)多分辨率块中帧族的构造,(2)特征提取器块中的新颖特征,(3)分类器块中已知分类器的多分辨率版本。此外,我们考虑的测试平台没有可用的自动分类工具。公共健康相关性:叙述 其动机是让生物和医学界可以使用该算法和软件工具箱来挖掘成像数据。由于我们的三个测试平台跨越了器官发育的广泛层次中的不同尺度,因此我们系统的成功将带来分子和细胞水平(果蝇项目)以及组织和器官水平(组织学和中耳炎项目)基础研究的进步)。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Local histograms and image occlusion models.
局部直方图和图像遮挡模型。
- DOI:
- 发表时间:2013-05-01
- 期刊:
- 影响因子:2.5
- 作者:Massar, Melody L;Bhagavatula, Ramamurthy;Fickus, Matthew;Kovačević, Jelena
- 通讯作者:Kovačević, Jelena
A domain-knowledge-inspired mathematical framework for the description and classification of H&E stained histopathology images.
用于描述和分类 H 的领域知识启发的数学框架
- DOI:
- 发表时间:2011-10-19
- 期刊:
- 影响因子:0
- 作者:Massar, Melody L;Bhagavatula, Ramamurthy;Ozolek, John A;Castro, Carlos A;Fickus, Matthew;Kovačević, Jelena
- 通讯作者:Kovačević, Jelena
Local Histograms for Classifying H&E Stained Tissues.
用于 H 分类的局部直方图
- DOI:
- 发表时间:2010-01-01
- 期刊:
- 影响因子:0
- 作者:Massar, M L;Bhagavatula, R;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.
H 中胚层成分的自动识别和描绘
- DOI:
- 发表时间:2010-04-14
- 期刊:
- 影响因子:0
- 作者:Bhagavatula, Ramamurthy;Fickus, Matthew;Kelly, W;Guo, Chenlei;Ozolek, John A;Castro, Carlos A;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:
- 发表时间:2010-05-01
- 期刊:
- 影响因子:0
- 作者:Sandryhaila, Aliaksei;Chebira, Amina;Milo, Christina;Kovčcević, Jelena;Püschel, Markus
- 通讯作者:Püschel, Markus
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JELENA KOVACEVIC其他文献
JELENA KOVACEVIC的其他文献
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{{ truncateString('JELENA KOVACEVIC', 18)}}的其他基金
IEEE International Symposium on Biomedical Imaging (ISBI) 2015
IEEE 国际生物医学成像研讨会 (ISBI) 2015
- 批准号:
8911701 - 财政年份:2015
- 资助金额:
$ 7.03万 - 项目类别:
Dx Ear: An automated tool for diagnosis of otitis media
Dx Ear:诊断中耳炎的自动化工具
- 批准号:
7908336 - 财政年份:2010
- 资助金额:
$ 7.03万 - 项目类别:
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:
7712998 - 财政年份:2009
- 资助金额:
$ 7.03万 - 项目类别:
AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS
荧光显微镜数据集的自动分割
- 批准号:
7513584 - 财政年份:2008
- 资助金额:
$ 7.03万 - 项目类别:
AUTOMATED SEGMENTATION OF FLUORESCENCE MICROSCOPY DATA SETS
荧光显微镜数据集的自动分割
- 批准号:
7632204 - 财政年份:2008
- 资助金额:
$ 7.03万 - 项目类别:
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