Partially cystic thyroid cancer on conventional and elastographic ultrasound: a retrospective study and a machine learning-assisted system.

Background: Thyroid carcinoma constitutes the vast majority of all thyroid cancer, most of which is the solid nodule type. No previous studies have examined combining both conventional and elastic sonography to evaluate the diagnostic performance of partially cystic thyroid cancer (PCTC). This retrospective study was designed to evaluate differentiation of PCTC from benign partially cystic nodules with a machine learning-assisted system based on ultrasound (US) and elastography.

[Hyperspectral Detection Model for Soil Dispersion in Zhouqu Debris Flow Source Region].

Sensitive band positions, models and the principles of soil dispersion detected by hyperspectral remote sensing were firstly discussed according to the results of soil dispersive hyperspectral remote sensing experiment. Results showed that, (1) signals and noises could be separated by Fourier transformation. A finely mineral identification system was developed to remove spectral noises and provide highly accurate data for establishing soil dispersive model; (2) Soil dispersive hyperspectral remote sensing model established by the multiple linear regression method was good at soil dispersion forecasting for the high correlation between sensitive bands and the soil dispersions.

[A Method to Extract Content of Minerals Based on Measured Hyperspectral Data].

To improve the accuracy of mineral content extraction by linear decomposition model, a method was established, which took rock spectra with wavelength from 350 to 2 500 nm as the data source, identified minerals based on spectral matching methods, applied Hapke model to transform spectral reflectance into single scattering albedo and resolved single scattering albedo to get mineral content. In this method, sectional noise filtering and regional mineral spectra library were added to improve the identifying accuracy. Based on the analysis on the fifth Baogutu rock body, compared with XRD results, accuracies of quartz, feldspar class and altered minerals identification were 75%, 100% and 92.

[A method of hyperspectral quantificational identification of minerals based on infrared spectral artificial immune calculation].

Rapid identification of minerals based on near infrared (NIR) and shortwave infrared (SWIR) hyperspectra is vital to remote sensing mine exploration, remote sensing minerals mapping and field geological documentation of drill core, and have leaded to many identification methods including spectral angle mapping (SAM), spectral distance mapping (SDM), spectral feature fitting(SFF), linear spectral mixture model (LSMM), mathematical combination feature spectral linear inversion model(CFSLIM) etc. However, limitations of these methods affect their actual applications. The present paper firstly gives a unified minerals components spectral inversion (MCSI) model based on target sample spectrum and standard endmember spectral library evaluated by spectral similarity indexes.

[Hyperspectral remote sensing image classification based on SVM optimized by clonal selection].

Model selection for support vector machine (SVM) involving kernel and the margin parameter values selection is usually time-consuming, impacts training efficiency of SVM model and final classification accuracies of SVM hyperspectral remote sensing image classifier greatly. Firstly, based on combinatorial optimization theory and cross-validation method, artificial immune clonal selection algorithm is introduced to the optimal selection of SVM (CSSVM) kernel parameter a and margin parameter C to improve the training efficiency of SVM model. Then an experiment of classifying AVIRIS in India Pine site of USA was performed for testing the novel CSSVM, as well as a traditional SVM classifier with general Grid Searching cross-validation method (GSSVM) for comparison.

[Study of using regional mineral spectra library and section noise filtering to improve mineral identification accuracy].

Aiming at the low accuracy of mineral identification with hyperspectral data, the present article established regional spectra library on the basis of the study area geological background, and presented a pretreatment method that filters the original spectra by section. First, continuum based fast Fourier transform was used to filter the noise among 2000-2200, 2250-2300 and 2350-2500 nm. Then apply the rapid quantificational identification model with regional spectrum library was used to dispose the processed spectra.

[The application of spectral geological profile in the alteration mapping].

Geological section can help validating and understanding of the alteration information which is extracted from remote sensing images. In the paper, the concept of spectral geological profile was introduced based on the principle of geological section and the method of spectral information extraction. The spectral profile can realize the storage and vision of spectra along the geological profile, but the spectral geological spectral profile includes more information besides the information of spectral profile.

[Study of spectrum preprocessing method when applying the characteristic spectrum linear inversion modeling to extract the mineral information].

The rapid identification of the minerals in the field is crucial in the remote sensing geology study and mineral exploration. The characteristic spectrum linear inversion modeling is able to obtain the mineral information quickly in the field study. However, the authors found that there was significant difference among the results of the model using the different kinds of spectra of the same sample.

[Alteration mineral mapping method based on statistical analysis of field measured spectra].

The present paper presents a new alteration mineral mapping method based on statistical analysis of field measured spectra. First of all, this method processes a cluster of measurement data of spectra of field samples, in order to distinguish different sample area from the overall types. Second, the results of the clustering of different mineral alterations established their respective discriminant functions.

[A novel hyperspectra absorption enhancing method based on morphological top-hat transformation].

Hyperspectral characteristics analysis of ground features is the basis for applications of high-resolution imaging technology to ground target identification and ground features classification. Based on morphological multi-scale Top-Hat transformation, a novel spectral absorption enhancing algorithms was put forward, which enhanced spectral absorption features while maintaining shape features of the absorption peak bands. Eleven reflectance spectra of different mineral groups were chosen from the mineral spectral library of the United States Geological Survey (USGS), and we used a K-means clustering analysis on both the absorption-enhanced spectra and the original reflectance spectra.

[Study on the geochemical anomaly of copper element based on reflectance spectra].

In order to explore the feasibility of studying the geochemical anomaly of copper element by using remote sensing method, the correlation between Cu and other elements and the correlation between Cu and reflectance spectra were analyzed based on the element contents and the reflectance spectra of rock samples. It was found that Fe is most highly correlated with Cu, followed by Ti and As. The relationship between the Cu content and the reflectance spectra is of a negative correlation, and the higher the Cu content, the stronger the correlation.

[A rapid quantificational identification model of minerals and its applications].

Rapid identification of minerals is the key point for enhancing the efficiency of mineral exploration by remote sensing, mineral mapping by remote sensing and many geological investigations. Because of the limitation of technology and other aspects, the amount of models and software concerning rapid identification of minerals is very small. Since 1990s the development in spectrometers and computers has made it possible to apply near infrared spectrum technology to identify minerals.

[Research on spectrum denoising methods based on the combination of wavelet package transformation and mathematical morphology].

The present study introduced the generalized morphological filter into the denoising of visible and near infrared spectra for the first time, and provided a new method for denoising the reflectance spectra by combining mathematical morphology methods with the wavelet packet transformation. The authors used vegetable spectra from USGS spectral library as the reference spectra, and obtained the noised spectra by adding noises with different signal-to-noise ratios to the referenced spectra. The results were evaluated by signal-to-noise ratio (SNR), root mean squared error (RMSE), normalized correlation coefficient (NCC) and smoothness ratio (SR) of the denoised spectra.

[Continuum based fast Fourier transform processing of infrared spectrum].

To recognize ground objects with infrared spectrum, high frequency noise removing is one of the most important phases in spectrum feature analysis and extraction. A new method for infrared spectrum preprocessing was given combining spectrum continuum processing and Fast Fourier Transform (CFFT). Continuum was firstly removed from the noise polluted infrared spectrum to standardize hyper-spectra.

[Comparison of two spectral mixture analysis models].

A spectral mixture analysis experiment was designed to compare the spectral unmixing effects of linear spectral mixture analysis (LSMA) and constraint linear spectral mixture analysis (CLSMA). In the experiment, red, green, blue and yellow colors were printed on a coarse album as four end members. Thirty nine mixed samples were made according to each end member's different percent in one pixel.

[An algorithm for highlightling structure in multispectral remote sensing].

Based on the principle of mineral generation, structures could provide not only passage ways for ore-forming fluid, but also space for them to aggregate. So, it was very important to study the feature of structures in study area before mineral exploration. In order to highlight structures using multispectral remote sensing data, an algorithm integrating principle component analysis (PCA), maximum noise fraction transformation (MNF) and original image data was proposed here.

[Wavelet analysis and its application in denoising the spectrum of hyperspectral image].

In order to remove the sawtoothed noise in the spectrum of hyperspectral remote sensing and improve the accuracy of information extraction using spectrum in the present research, the spectrum of vegetation in the USGS (United States Geological Survey) spectrum library was used to simulate the performance of wavelet denoising. These spectra were measured by a custom-modified and computer-controlled Beckman spectrometer at the USGS Denver Spectroscopy Lab. The wavelength accuracy is about 5 nm in the NIR and 2 nm in the visible.

[A target-highlighting method in multispectral remote sensing].

In order to highlight target in multispectral remote sensing and overcome the human error caused by threshold, a new method is proposed here. Image of target similarity is firstly calculated by spectral energy level matching (SEM) algorithm and as a band added to original image; Then, band normalization is performed on the new image to reduce the effects caused by the order of magnitude in different bands; Finally, a false color image that highlights the target is made by RGB composed of the first three bands (3, 2, 1) in MNF transformation. Results from the experiment of highlighting the main rock-type tuffaceous siltstone in Hatu area, Xinjiang province, China show that (1) the new method can highlight target for the increment of target's information and weights during the process of transformation by adding a band representing target's similarity to the original image.

[Analysis on possibilities of multi-spectral data for quantitative retrieving soil nutrition element contents].

Models for predicting soil nutrition elements content were established by regression methods. The data source was simulated multi-spectral data from reflectance spectra measured under laboratory condition. First, the reflectance spectra were resampled to the corresponding bands of multi-spectral sensors (TM and ASTER) according to their reflectance response functions.