Mueller matrix imaging is emerging for the quantitative characterization of pathological microstructures and is especially sensitive to fibrous structures. Liver fibrosis is a characteristic of many types of chronic liver diseases. The clinical diagnosis of liver fibrosis requires time-consuming multiple staining processes that specifically target on fibrous structures. The staining proficiency of technicians and the subjective visualization of pathologists may bring inconsistency to clinical diagnosis. Mueller matrix imaging can reduce the multiple staining processes and provide quantitative diagnostic indicators to characterize liver fibrosis tissues. In this study, a fiber-sensitive polarization feature parameter (PFP) was derived through the forward sequential feature selection (SFS) and linear discriminant analysis (LDA) to target on the identification of fibrous structures. Then, the Pearson correlation coefficients and the statistical T-tests between the fiber-sensitive PFP image textures and the liver fibrosis tissues were calculated. The results show the gray level run length matrix (GLRLM)-based run entropy that measures the heterogeneity of the PFP image was most correlated to the changes of liver fibrosis tissues at four stages with a Pearson correlation of 0.6919. The results also indicate the highest Pearson correlation of 0.9996 was achieved through the linear regression predictions of the combination of the PFP image textures. This study demonstrates the potential of deriving a fiber-sensitive PFP to reduce the multiple staining process and provide textures-based quantitative diagnostic indicators for the staging of liver fibrosis.

Ovarian cancer is one of the most aggressive and heterogeneous female tumors in the world, and serous ovarian cancer (SOC) is of particular concern for being the leading cause of ovarian cancer death. Due to its clinical and biological complexities, ovarian cancer is still considered one of the most difficult tumors to diagnose and manage. In this study, three datasets were assembled, including 30 cases of serous cystadenoma (SCA), 30 cases of serous borderline tumor (SBT), and 45 cases of serous adenocarcinoma (SAC). Mueller matrix microscopy is used to obtain the polarimetry basis parameters (PBPs) of each case, combined with a machine learning (ML) model to derive the polarimetry feature parameters (PFPs) for distinguishing serous ovarian tumor (SOT). The correlation between the mean values of PBPs and the clinicopathological features of serous ovarian cancer was analyzed. The accuracies of PFPs obtained from three types of SOT for identifying dichotomous groups (SCA versus SAC, SCA versus SBT, and SBT versus SAC) were 0.91, 0.92, and 0.8, respectively. The accuracy of PFP for identifying triadic groups (SCA versus SBT versus SAC) was 0.75. Correlation analysis between PBPs and the clinicopathological features of SOC was performed. There were correlations between some PBPs (δ, β, qL, E2, rq_cross, P2, P3, P4, and P5) and clinicopathological features, including the International Federation of Gynecology and Obstetrics (FIGO) stage, pathological grading, preoperative ascites, malignant ascites, and peritoneal implantation. The research showed that PFPs extracted from polarization images have potential applications in quantitatively differentiating the SOTs. These polarimetry basis parameters related to the clinicopathological features of SOC can be used as prognostic factors.

High-grade squamous intraepithelial lesion (HSIL) is regarded as a serious precancerous state of cervix, and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis and treatment of cervical lesions. Pathologists examine the biopsied cervical epithelial tissue through a microscope. The pathological examination will take a long time and sometimes results in high inter- and intra-observer variability in outcomes. Polarization imaging techniques have broad application prospects for biomedical diagnosis such as breast, liver, colon, thyroid and so on. In our team, we have derived polarimetry feature parameters (PFPs) to characterize microstructural features in histological sections of breast tissues, and the accuracy for PFPs ranges from 0.82 to 0.91. Therefore, the aim of this paper is to distinguish automatically microstructural features between HSIL and cervical squamous cell carcinoma (CSCC) by means of polarization imaging techniques, and try to provide quantitative reference index for pathological diagnosis which can alleviate the workload of pathologists. Polarization images of the H&E stained histological slices were obtained by Mueller matrix microscope. The typical pathological structure area was labeled by two experienced pathologists. Calculate the polarimetry basis parameter (PBP) statistics for this region. The PBP statistics (stat PBPs) are screened by mutual information (MI) method. The training method is based on a linear discriminant analysis (LDA) classifier which finds the most simplified linear combination from these stat PBPs and the accuracy remains constant to characterize the specific microstructural feature quantitatively in cervical squamous epithelium. We present results from 37 clinical patients with analysis regions of cervical squamous epithelium. The accuracy of PFP for recognizing HSIL and CSCC was 83.8% and 87.5%, respectively. This work demonstrates the ability of PFP to quantitatively characterize the cervical squamous epithelial lesions in the H&E pathological sections. Significance: Polarization detection technology provides an e±cient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections.

对艾比湖湿地自然保护区的芦苇叶片进行了采样、含水量测定，利用FieldSpec3便携式光谱仪现场进行了芦苇叶片的光谱测量，并对芦苇叶片原始光谱进行了平滑处理、导数变换，分析在不同微分窗口尺度下芦苇叶片一阶导数对含水量变化的响应特征，同时提取特征参数表征芦苇叶片含水量。研究表明：当w=1~15时，一阶光谱曲线噪声难以有效降低，致使光谱曲线的轮廓不易辨别，叶片含水量变化引起的光谱响应特征不易判断。当w=16~30时，微分窗口尺度的增大较好地消除了芦苇叶片一阶导数光谱值中的噪声，芦苇叶片一阶导数光谱与含水量之间的相关系数在1460~1500 nm波段与1374~1534 nm 波段相比，相关系数波动小，标准差小，表现较稳定，1460~1500 nm波段一阶导数光谱对含水量的响应呈现比较一致、显著的特征；将1460~1500 nm波段内一阶导数光谱值的平均值作为特征参数，在微分窗口尺度1~30下，该特征参数与其他窗口尺度相比，更适合指示芦苇叶片含水量变化，为干旱区湿地芦苇水分遥感监测中采用短波红外反射光谱分析技术定量检验芦苇叶片水分提供新的途径。

高斯分解是波形激光雷达数据预处理的常用方法, 但在应用于大光斑全波形激光雷达数据中的叠加波时却难以发挥作用, 为此提出一种基于小波变换的高斯递进波形分解方法.首先, 利用小波变换多尺度分析特性检测出目标地物并准确估算组分特征参数, 进而建立高斯模型优化特征参数；然后通过拟合精度指标, 判断是否需要添加新组分进行逐级递进分解, 确定最终模型及其组分构成, 最终实现全波形激光雷达数据的波形分解.为了验证算法的有效性, 分别对实验数据使用本文算法和常用的基于拐点匹配的高斯分解法进行分析, 结果表明, 本文算法提取的目标数几乎是拐点匹配算法的2倍, 可以有效地从叠加波中检测出目标组分, 且拟合精度高于98%.

根据散射光成像原理,采用大小两个视场来获取不同精度的暗背景下的亮疵病图像,设计了完整的数字化表面疵病检测系统。该系统采用多区域自适应阈值分割算法对图像进行分割,然后采用基于等价归并标记方法快速提取疵病的特征参数,最后利用BP神经网络对疵病进行分类。实验结果表明该方法既满足实时性需求,又取得了较好的分类检测效果。