Vector Quantization for Image Pattern Recognition

描述(由申请人提供)： 这一第一阶段SBIR应用解决了病理学家和临床医生在手动检查显微镜载玻片方面面临的日益重大的挑战。显微镜检查是劳动密集型的、主观的、昂贵的，而且受到实际接触感兴趣的玻璃片样品的限制。自动化显微镜检查的障碍一直是无法以高分辨率高效地数字化整个显微镜标本。Aperio开发了ScanScope(R)，这是一种新型的显微镜载玻片扫描仪，首次使其能够以高分辨率快速创建虚拟显微镜载玻片。虚拟幻灯片为使用自动模式识别的自动显微检查奠定了基础。本研究旨在调整和优化Aperio现有的和新的矢量量化(VQ)算法，以解决虚拟幻灯片中的自动模式识别问题。VQ是使用词汇表对比特流进行编码的通用数学技术。其主要目的是证明在实际和特征良好的应用中使用VQ进行模式识别的可行性：自动发现细胞学样本中几乎所有的微转移簇。这项拟议的研究代表了使用VQ自动识别虚拟幻灯片中的模式的首次尝试。

DESCRIPTION (provided by applicant): This Phase-I SBIR application addresses the increasingly significant challenges faced by pathologists and clinicians in manually inspecting microscope slides. Microscopic inspection suffers from being labor-intensive, subjective, expensive and limited by the need for physical access to the glass slide specimen of interest. The obstacle to automated microscopic inspection has been the inability to efficiently digitize entire microscope specimens at high resolutions. Aperio has developed the ScanScope (R), a novel microscope slide scanner that makes it practical - for the first time - to rapidly create virtual microscope slides at high resolutions. Virtual slides set the stage for automating microscopic inspection using automated pattern recognition. This research aims to adapt and optimize Aperio's existing and novel algorithms for vector quantization (VQ) to the problem of automatic pattern recognition in virtual slides. VQ is a general mathematical technique for encoding bitstreams using a vocabulary. The primary aim is to demonstrate the feasibility of using VQ for pattern recognition in a practical and well-characterized application: automatically finding virtually all micrometastasis clusters in cytology specimens. This proposed research represents a first attempt to automate pattern recognition in virtual slides using VQ.