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Diffuse reflectance imaging modalities for characterization of highly scattering turbid media.

机译：漫反射成像模式用于表征高度散射的混浊介质。

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摘要

Two different novel diffuse reflectance imaging modalities were studied as potential tools to characterize highly scattering turbid media. In the first case, diffuse reflectance polarization imaging (DRPI) was introduced to detect changes in optical properties in tissue-simulating phantoms. The changes in polarization patterns were correlated with the concentration of added molecules to the tissue phantoms. Radial and angular image analysis procedures were developed for these symmetric images to quantify variations in polarization patterns. This study showed that DRPI can be used as a tool to measure concentration changes of single compounds in turbid media. The findings of this basic research can be extended to real biological tissues to monitor the concentration changes of molecules, such as glucose, important for diabetic patients.; In the second study, oblique incidence diffuse reflectance spectroscopic imaging (DRSI) modality was employed to identify benign and cancerous skin lesions. In this imaging technique, multiple fibers were used to collect diffusely reflected light from the skin surface. The light collected by each fiber was separated into its spectral components with an imaging spectogram. Combining the 1-D spectral signals with multiple fibers provided a 2-D image that could then be analyzed using both spectrum as well as image analysis techniques, providing both frequency and spatial information. Unknown lesions were separated by developing image analysis, feature extraction, and classification techniques. For these images, texture features were investigated and new features introduced to test their success in separating the two classes. A region search algorithm was developed to find the optimum location of the region and width of region that produced features for best class separation. Over 95% correct classification rate was reached by the designed bootstrap-based Bayes classifier. Comparable results were obtained with the neural network classifier. 100% sensitivity and over 93% specificity was achieved. The results showed that the DRSI technique, when combined with feature extraction and classification algorithm, can be used to identify benign and cancerous skin lesions, and provide an alternative second opinion to the dermatologist on suspicious skin lesions in clinical settings.
机译：研究了两种不同的新型漫反射成像方式，作为表征高散射混浊介质的潜在工具。在第一种情况下，引入了漫反射偏振成像（DRPI）以检测组织模拟体模中光学特性的变化。偏振模式的变化与组织体模中添加分子的浓度相关。针对这些对称图像开发了径向和角度图像分析程序，以量化偏振图案的变化。这项研究表明，DRPI可用作测量混浊介质中单个化合物浓度变化的工具。该基础研究的发现可以扩展到实际的生物组织，以监测对糖尿病患者重要的分子（例如葡萄糖）的浓度变化。在第二项研究中，采用斜入射漫反射光谱成像（DRSI）方式识别良性和癌性皮肤病变。在这种成像技术中，使用了多根纤维来收集来自皮肤表面的漫反射光。每根光纤收集的光通过成像光谱图分成其光谱成分。将一维光谱信号与多根光纤组合在一起可提供二维图像，然后可以使用频谱和图像分析技术对二维图像进行分析，从而提供频率和空间信息。通过显影图像分析，特征提取和分类技术来分离未知病变。对于这些图像，对纹理特征进行了研究，并引入了新特征以测试它们在分离这两个类别中的成功。开发了一种区域搜索算法，以找到区域的最佳位置和区域宽度，以产生最佳类别分离的特征。设计的基于引导程序的贝叶斯分类器可达到95％以上的正确分类率。使用神经网络分类器获得了可比的结果。获得了100％的敏感性和超过93％的特异性。结果表明，DRSI技术与特征提取和分类算法相结合，可用于识别良性和癌性皮肤病变，并为皮肤科医生提供有关临床环境中可疑皮肤病变的另类意见。

著录项

  • 作者

    Mehrubeoglu, Mehrube.;

  • 作者单位

    Texas A&M University.;

  • 授予单位 Texas A&M University.;
  • 学科 Engineering Electronics and Electrical.; Engineering Biomedical.; Health Sciences Medicine and Surgery.
  • 学位 Ph.D.
  • 年度 2000
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;生物医学工程;
  • 关键词

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