Algorithms and Image Analysis Software Tool for Automated Recognition and Identif

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。虽然这个宝藏隐藏了许多重要问题的答案，但可视化地挖掘它是一项巨大且容易出错的任务，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一个标准任务，是医学和生物学中许多问题的基础，例如基于亚细胞位置模式识别蛋白质，确定果蝇胚胎的发育阶段，识别组织学中的组织和中耳炎的诊断。由此可见：我们建议开发一个灵活的，模块化的和准确的算法和软件工具箱，自动识别和识别正常和病理过程中发生的疾病和发展。通用分类系统计算一组描述数据的数值特征，然后将这些特征分成类别。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有价值的信息。每个子空间执行单独的分类，给出其投票。将这些局部投票协调成单个全局投票的仲裁器是加权块。它根据每个子空间在训练过程中投票的可靠性为每个子空间分配权重。基于我们的初步工作，我们相信该系统具有很大的潜力，准确和强大的分类（识别，鉴定）的正常和病理过程中发生的疾病和发展。具体目标1：提出了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，实现效率高。具体目标二：开发一个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使用，以及他们与算法开发人员的互动。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开发的广泛层次内的广泛应用中广泛使用。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。创新的工作带来的。我们提出的算法是灵活的，准确的和新颖的：多分辨率工具提供了一个窗口，以前看不见的功能在一个数据集。多分辨率分类器的每个块将提供新颖的贡献：（1）在多分辨率块中构造帧族，（2）在特征提取器块中构造新颖特征，（3）在分类器块中构造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。公共卫生相关性：叙述的动机是这个算法和软件工具箱可用于生物和医学界挖掘成像数据。由于我们的三个试验平台跨越器官发育的广泛层次内的各种尺度，我们的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）的基础研究的进步。

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).