Polarimetry feature parameter deriving from Mueller matrix imaging and auto-diagnostic signicance to distinguish HSIL and CSCC

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 patho-logical 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 path-ological 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)classier whichnds the most simplied linear combination from these stat PBPs and the accuracy remains constant to characterize the specic 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 charac-terize the cervical squamous epithelial lesions in the H&E pathological sections.Signicance:Polarization detection technology provides an effcient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections.

Identification of serous ovarian tumors based on polarization imaging and correlation analysis with clinicopathological features

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 di±cult 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,q_(L),E_(2),rqcross,P_(2),P_(3),P_(4),and P_(5))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.

Correlation of image textures of a polarization feature parameter and the microstructures of liver fibrosis tissues

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 fibersensitive 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 coeffcients 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.

Classification of wood surface texture based on Gauss-MRF Model

The basal theory of Gauss-MRF is expounded and 2-5 order Gauss MRF models are established. Parameters of the 2-5 order Gauss-MRF models for 300 wood samples＇ surface texture are also estimated by using LMS. The data analysis shows that： 1） different rexture parameters have a clear scattered distribution, 2） the main direction of texture is the direction represented by the maximum parameter of Gauss-MRF parameters, and 3） for those samples having the same main direction, the finer the texture is, the greater the corresponding parameter is, and the smaller the other parameters are; and the higher the order of Gauss-MRF is, the more clearly the texture is described. On the condition of the second order Gauss MRF model, parameter B1, B2 of tangential texture are smaller than that of radial texture, while B3 and B4 of tangential texture are greater than that of radial texture. According to the value of separated criterion, the parameter of the fifth order Gauss-MRF is used as feature vector for Hamming neural network classification. As a result, the ratio of correctness reaches 88%.

Application of Particle Swarm Optimization to Fault Condition Recognition Based on Kernel Principal Component Analysis

Panicle swarm optimization （PSO） is an optimization algorithm based on the swarm intelligent principle. In this paper the modified PSO is applied to a kernel principal component analysis （ KPCA ） for an optimal kernel function parameter. We first comprehensively considered within-class scatter and between-class scatter of the sample features. Then, the fitness function of an optimized kernel function parameter is constructed, and the particle swarm optimization algorithm with adaptive acceleration （CPSO） is applied to optimizing it. It is used for gearbox condi- tion recognition, and the result is compared with the recognized results based on principal component analysis （PCA）. The results show that KPCA optimized by CPSO can effectively recognize fault conditions of the gearbox by reducing bind set-up of the kernel function parameter, and its results of fault recognition outperform those of PCA. We draw the conclusion that KPCA based on CPSO has an advantage in nonlinear feature extraction of mechanical failure, and is helpful for fault condition recognition of complicated machines.

An improved empirical wavelet transform method for rolling bearing fault diagnosis

Empirical wavelet transform(EWT)based on the scale space method has been widely used in rolling bearing fault diagnosis.However,using the scale space method to divide the frequency band,the redundant components can easily be separated,causing the band to rupture and making it difficult to extract rolling bearing fault characteristic frequency effectively.This paper develops a method for optimizing the frequency band region based on the frequency domain feature parameter set.The frequency domain feature parameter set includes two characteristic parameters:mean and variance.After adaptively dividing the frequency band by the scale space method,the mean and variance of each band are calculated.Sub-bands with mean and variance less than the main frequency band are combined with surrounding bands for subsequent analysis.An adaptive empirical wavelet filter on each frequency band is established to obtain the corresponding empirical mode.The margin factor sensitive to the shock pulse signal is introduced into the screening of empirical modes.The empirical mode with the largest margin factor is selected to envelope spectrum analysis.Simulation and experiment data show this method avoids over-segmentation and redundancy and can extract the fault characteristic frequency easier compared with only scale space methods.

Application of biomonitoring and support vector machine in water quality assessment

The behavior of schools of zebrafish （Danio rerio） was studied in acute toxicity environments. Behavioral features were extracted and a method for water quality assessment using support vector machine （SVM） was de- veloped. The behavioral parameters of fish were recorded and analyzed during one hour in an environment of a 24-h half-lethal concentration （LC50） of a pollutant. The data were used to develop a method to evaluate water quality, so as 6＋ 2＋ to give an early indication of toxicity. Four kinds of metal ions （Cu2~, Hg2~, Cr , and Cd ） were used for toxicity testing. To enhance the efficiency and accuracy of assessment, a method combining SVM and a genetic algorithm （GA） was used. The results showed that the average prediction accuracy of the method was over 80% and the time cost was acceptable. The method gave satisfactory results for a variety of metal pollutants, demonstrating that this is an effective approach to the classification of water quality.

Cooperative automatic modulation recognition in cognitive radio

In this article, a new effective method of cooperative modulation recognition （CMR） is proposed to recognize different modulation types of primary user for cognitive radio receivers. In the cognitive radio （CR） system, two CR users respectively send their feature parameters to the cooperative recognition center, which is composed of back propagation neural network （BPNN）. With two users＇ cooperation and the application of an error back propagation learning algorithm with momentum, the center improves the performance of modulation recognition, especially when one of the CR users＇ signal-to-noise ratio （SNR） is low. To measure the performance of the proposed method, simulations are carried out to classify different types of modulated signals corrupted by additive white Gaussian noise （AWGN）. The simulation results show that this cooperation algorithm has a better recognition performance than those without cooperation.