Recognition of Heterogeneous Edges in Multiwavelength Transmission Images Based on the Weighted Constraint Decision Method.

Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

Accuracy improvement of quantitative analysis in VIS-NIR spectroscopy using the GKF-WTEF algorithm.

The extraction of effective information in visible-near-infrared (VIS-NIR) spectroscopy is crucial and difficult for spectral analysis. In this research, an algorithm of wavelet feature extraction based on the Gaussian kernel function (GKF-WTEF) was developed to suppress the influence of external interference on VIS-NIR spectroscopy and improve the accuracy of quantitative analysis. This algorithm takes the root-mean-square error of the prediction set (RMSEP) of the model, which is established by partial least-squares regression, as the optimization criteria. First, the optimal type of wavelet function, the decomposition level, and the Gauss kernel function central frequency band are determined according to the RMSEP. Second, the Gauss kernel function bandwidth is determined by Newton's method. Then, the Hadamard product of the Gaussian kernel function and the wavelet coefficient is obtained. Finally, the wavelet coefficients after the Hadamard product can be reconstructed to obtain the spectral data after feature extraction. In order to verify the effectiveness of this algorithm, the difference in the optical parameters of the polyvinyl chloride material container was used as an external interference source. And the spectrum of Intra-lipid and India-ink mixed solution with different concentrations was collected therein. The volume fraction of India-ink in complex mixed solution was quantitatively analyzed by using the RMSEP and the average relative error of the prediction set as the evaluation criteria. The research results demonstrated that the Gaussian-wavelet transform feature extraction algorithm is an effective pretreatment method, it can satisfactorily suppress the influence of external interference on the spectrum, and it can improve the analytical accuracy of VIS-NIR spectroscopy.

A novel similarity comparison approach for dynamic ECG series.

The heart sound signal is a reflection of heart and vascular system motion. Long-term continuous electrocardiogram (ECG) contains important information which can be helpful to prevent heart failure. A single piece of a long-term ECG recording usually consists of more than one hundred thousand data points in length, making it difficult to derive hidden features that may be reflected through dynamic ECG monitoring, which is also very time-consuming to analyze. In this paper, a Dynamic Time Warping based on MapReduce (MRDTW) is proposed to make prognoses of possible lesions in patients. Through comparison of a real-time ECG of a patient with the reference sets of normal and problematic cardiac waveforms, the experimental results reveal that our approach not only retains high accuracy, but also greatly improves the efficiency of the similarity measure in dynamic ECG series.

Symbolic representation based on trend features for biomedical data classification.

BACKGROUND: The widespread access to portable medical devices or new personal devices is boosting the amount of biomedical data. These devices provide a growing massive data that far exceeds the analytical ability of a professional doctor. The computer-assisted analysis of biomedical data has become an essential tool in medicine diagnosis. OBJECTIVE: Due to the advantages of discrete, noise elimination and dimensionality reduction, symbolic representation of biomedical data has attracted great interest. The symbolization results provide efficiently performing at data mining, such as pattern discovery, anomaly detection and association rules mining, so we want to use the method to improving the biomedical data classification. METHODS: In this paper, we introduce a novel symbolic representation method, called Trend Feature Symbolic Approximation (TFSA). RESULTS: The TFSA focuses on retaining most of the original series' trend features, and it also very suitable for subsequent mining work, such as association rules mining. CONCLUSION: The TFSA provides the lower bounding guarantee and the experimental results show that comparing with some existing methods, its classification accuracy is improved.

Satellite fault diagnosis using support vector machines based on a hybrid voting mechanism.

The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.

Gross alpha and beta activity analyses in urine-a routine laboratory method for internal human radioactivity detection.

The aim of this work was to develop a method to provide rapid results for humans with internal radioactive contamination. The authors hypothesized that valuable information could be obtained from gas proportional counter techniques by screening urine samples from potentially exposed individuals rapidly. Recommended gross alpha and beta activity screening methods generally employ gas proportional counting techniques. Based on International Standards Organization (ISO) methods, improvements were made in the evaporation process to develop a method to provide rapid results, adequate sensitivity, and minimum sample preparation and operator intervention for humans with internal radioactive contamination. The method described by an American National Standards Institute publication was used to calibrate the gas proportional counter, and urine samples from patients with or without radionuclide treatment were measured to validate the method. By improving the evaporation process, the time required to perform the assay was reduced dramatically. Compared with the reference data, the results of the validation samples were very satisfactory with respect to gross-alpha and gross-beta activities. The gas flow proportional counting method described here has the potential for radioactivity monitoring in the body. This method was easy, efficient, and fast, and its application is of great utility in determining whether a sample should be analyzed by a more complicated method, for example radiochemical and/or γ-spectroscopy. In the future, it may be used commonly in medical examination and nuclear emergency treatment.Health Phys. 106(5):000-000; 2014.

Enhanced photocurrent in thin-film amorphous silicon solar cells via shape controlled three-dimensional nanostructures.

In this paper, we have explored manufacturable approaches to sub-wavelength controlled three-dimensional (3D) nano-patterns with the goal of significantly enhancing the photocurrent in amorphous silicon solar cells. Here we demonstrate efficiency enhancement of about 50% over typical flat a-Si thin-film solar cells, and report an enhancement of 20% in optical absorption over Asahi textured glass by fabricating sub-wavelength nano-patterned a-Si on glass substrates. External quantum efficiency showed superior results for the 3D nano-patterned thin-film solar cells due to enhancement of broadband optical absorption. The results further indicate that this enhanced light trapping is achieved with minimal parasitic absorption losses in the deposited transparent conductive oxide for the nano-patterned substrate thin-film amorphous silicon solar cell configuration. Optical simulations are in good agreement with experimental results, and also show a significant enhancement in optical absorption, quantum efficiency and photocurrent.