Search

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. Methods Patients with suspicious partially cystic nodules and finally confirmed were included in the study. We performed conventional US and real-time elastography (RTE). The US features of nodules were recorded. The data set was entered into 6 machine-learning algorithms. Sensitivity, specificity, accuracy, and area under the curve (AUC) were calculated. Results A total of 177 nodules were included in this study. Among these nodules, 81 were malignant and 96 were benign. Wreath-shaped feature, micro-calcification, and strain ratio (SR) value were the most important imaging features in differential diagnosis. The random forest classifier was the best diagnostic model. Conclusions US features of PCTC exhibited unique characteristics. Wreath-shaped partially cystic nodules, especially with the appearance of micro-calcifications and larger SR value, are more likely to be malignant. The random forest classifier might be useful to diagnose PCTC.

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. Then taking LSMM and SAM evaluation index for example, a specific formulation of unified MCSI model is presented in the form of a kind of combinatorial optimization. And then, an artificial immune colonial selection algorithm is used for solving minerals feature spectral linear inversion model optimization problem, which is named ICSFSLIM. Finally, an experiment was performed to use ICSFSLIM and CFSLIM to identify the contained minerals of 22 rock samples selected in Baogutu in Xinjiang China. The mean value of correctness and validness identification of ICSFSLIM are 34.22% and 54.08% respectively, which is better than that of CFSLIM 31.97% and 37.38%; the correctness and validness variance of ICSFSLIM are 0.11 and 0.13 smaller than that of CFSLIM, 0.15 and 0.25, indicating better identification stability.

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. And then, evaluation indexes including SVM model training time, classification overall accuracy (OA) and Kappa index of both CSSVM and GSSVM were all analyzed quantitatively. It is demonstrated that OA of CSSVM on test samples and whole image are 85.1% and 81.58, the differences from that of GSSVM are both within 0.08% respectively; And Kappa indexes reach 0.8213 and 0.7728, the differences from that of GSSVM are both within 0.001; While the ratio of model training time of CSSVM and GSSVM is between 1/6 and 1/10. Therefore, CSSVM is fast and accurate algorithm for hyperspectral image classification and is superior to GSSVM.

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. The main object of spectral geological spectral profile is to obtain the distribution of alteration types and content of minerals along the profile which can be extracted from spectra measured by field spectrometer, especially for the spatial distribution and mode of alteration association. Technical method and work flow of alteration information extraction was studied for the spectral geological profile. The spectral geological profile was set up using the ground reflectance spectra and the alteration information was extracted from the remote sensing image with the help of typical spectra geological profile. At last the meaning and effect of the spectral geological profile was discussed.

In this paper, we focused on the alunite alteration recognition which was characteristic of hydrothermal alteration zones in the study area of Cuprite mining area in southwestern of Nevada. Two solutions were practiced to improve the alteration recognition precision and reliability. One is the modeling of object reflectance spectra with the combination of linear and nonlinear model, which was taken as reference spectra instead of the mineral spectra from spectral library, another is the fusion of the information extraction methods including spectral characteristic analysis and spectral matching by the weights decision function. Several rule images were obtained in which each pixel representing the similarity between the corresponding pixel in the hyperspectral image to a reference spectra. An alteration map with improved precision was created after weights decision processing.

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. The present paper mainly studied the continuum based fast Fourier transform processing (CFFT) method and the characteristic spectrum linear inversion modeling (CSLM). On one hand, the authors obtained the optimal preferences of the CFFT method when applying it to rock samples: setting the CFFT low-pass frequency to 150 Hz. On the other hand, through the evaluation and study of the results of CSLM using different spectra, the authors found that the ASD spectra which were denoised in the CFFT method could provide better results when using them to extract the mineral information in the field.

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. Results showed that, firstly, clustering groups of the absorption-enhanced spectra (AES) had better similarity within the same clustering group, and greater difference between different groups, furthermore, they were more consistent with the geological background of these minerals compared with clustering result of the original spectra (OS). Secondly, while all the original spectra were re-sampled to their ASTER spectra and the AES clustering result was displayed in the form of ASTER spectra of the minerals, we could easily describe both the representative spectral feature of each clustering group, and the typical spectral differences between every two groups. These fully demonstrate that the absorption-enhanced spectra have enhanced absorption features of the mineral spectra, and improved the separability of hyper-spectra. Accordingly, feature analysis based on absorption enhanced spectra can be used as reference for information extracting based on multi-spectral remote sensing image data, and it is a very useful method of hyperspectral analysis.

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. Two models have emerged. Model I is based on analyzing the position of absorption bands, while Model II is founded on waveform matching. In the present paper, characteristic spectrum linear inversion modeling was built. Validated by the data gained from end-members of USGS mineral spectrum library by mixing randomly, this model with the accuracy being approximately 100% is much better than Model I and II. Used to analyze the 23 samples selected in Baogutu area in Xinjiang, the model we built with the accuracy of 64.6% is superior to Model I (the accuracy is 33.8%) and Model II (the accuracy is 8.1%). Though the accuracy of our model is not as high as that of identification by microscope at present, using our model is much more effective and convenient, and there also will be less artificial error and smaller workload. The good performance of our model in the mineral exploration work by remote sensing in Baogutu area in Xinjiang shows wide popularizing prospects.

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. Furthermore, based on the reflectance spectra, the partial least squares regression of the Cu, Fe, Ti and As content was carried out respectively. The result shows that Ti gets the highest accuracy, followed by Fe. The worst is for As. Although the accuracy of the Cu model is not too high, it is feasible to establish an indirect model of copper anomaly on the basis of Fe model because of the strong correlation between them. In order to improve the accuracy of the model, some transformations for the reflectance spectra were performed and many spectral indices were acquired. Based on the spectral indices, the partial least squares regression of Fe was carried out. The accuracy of the regression model increased greatly. The highest correlation coefficient of the regression model is 0.687 6 for the calibration samples and it is 0.595 9 for the validation samples.

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. Then the spectrum was transformed into frequency domain (FD) with fast Fourier transform (FFT), separating noise information from target information After noise eliminating from useful information with a low-pass filter, the filtered FD spectrum was transformed into time domain (TD) with fast Fourier inverse transform. Finally the continuum was recovered to the spectrum, and the filtered infrared spectrum was achieved. Experiment was performed for chlorite spectrum in USGS polluted with two kinds of simulated white noise to validate the filtering ability of CFFT by contrast with cubic function of five point (CFFP) in time domain and traditional FFT in frequency domain. A circle of CFFP has limited filtering effect, so it should work much with more circles and consume more time to achieve better filtering result. As for conventional FFT, Gibbs phenomenon has great effect on preprocessing result at edge bands because of special character of rock or mineral spectra, while works well at middle bands. Mean squared error of CFFT is 0. 000 012 336 with cut-off frequency of 150, while that of FFT and CFFP is 0. 000 061 074 with cut-off frequency of 150 and 0.000 022 963 with 150 working circles respectively. Besides the filtering result of CFFT can be improved by adjusting the filter cut-off frequency, and has little effect on working time. The CFFT method overcomes the Gibbs problem of FFT in spectrum filtering, and can be more convenient, dependable, and effective than traditional TD filter methods.

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. In the algorithm, the original image was firstly transformed by PCA and MNF; then all bands were normalized to reduce errors caused by different band dimensions, and three bands containing detailed structure information were selected to form the false color image in which structures in study area were highlighted. Results of transformation on enhanced thematic mapper (ETM) data acquired on June 27th 2000 in Hatu area, Xinjiang province, China showed that (1) the transformed image was not only more colorful than the original data, but also more gradational than the original data. (2) The color difference among objects was enhanced by the algorithm. (3) Structrues were highlighted by the algorithm. Therefore, the algorithm's effect of highlighting structures in study area was noticeable.

To use target information for space transformation in remote sensing data field, artificial immune network theory is introduced to multi-spectral remote sensing information mining, based on the knowledge of target spectrum. First, the target spectrums are fuzzy clustered into several subclasses, to retain different features of target in different subclasses. Then we develop a novel Regional-memory-pattern Artificial Immune Idiotypic Network (RAIN) model based on artificial idiotypic network theory, and train RAIN with subclasses samples. And then, the affinities of the target spectrum and other objects can be calculated according to the immune microscopic dynamics including stimulation and suppression effect. Finally, principal component analysis (PCA) is performed to affinities to explore more weak and hidden information. With its application in Baoguto Area, Xinjiang Uyghur Autonomous Region China, choosing tuffaceous siltstone as target object, the result supports the efficiency of the RAIN-affinity-PCA scheme.

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. Then, the experiential models were established between measured spectra, simulated reflectance spectra (TM and ASTER) and soil nutrition element contents by stepwise multiple linear regression (SMLR) and partial least square regression (PLSR) methods. Precision of these models was tested by validation soil samples. Compared with models established by measured spectra, precision of simulated spectra models is slightly affected by spectral resolution. Simulated spectra models give good results for nitrogen (R = 0.89), phosphor (R = 0.79), and potassium (R = 0.68). The selected band range of SMLR models for soil N, P, and K are 2 000 to 2 300 nm, 1 650 to 1 800 nm and 600 to 800 nm respectively. The coefficients of PLSR models show that near infrared (NIR) band is more sensitive to nitrogen and phosphor than visible (VIS) band, while VIS is better for potassium. Good prediction performance indicates theoretically the future possibilities of multivariate calibration for soil nutrition element concentrations by multi-spectral remotely sensed images and bands character of sensors should be considered well because different element has different response.