- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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An effective image denoising using PPCA and classification of CT images using artificial neural networks
摘要： The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.
关键词： GLCM,median filter,Gaussian noise,pixel surge model,CT images,neural networks,image denoising,PPCA
更新于2025-09-04 15:30:14
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Haze Removal of Single Remote Sensing Image by Combining Dark Channel Prior with Superpixel
摘要： Dehazing is important in remote sensing image restorations to enhance the acquired low quality image for interpretation. However, traditional methods have some limitations for dehazing of remote sensing images due to its color distortion and noise. In this paper, we propose an improved method combining superpixel segmentation with luminance information of a haze image to estimate the atmospheric light instead of dark channel prior. Using this method with the haze imaging model, we can directly estimate the thickness of the haze and restore a high quality haze-free image. Experimental results on a variety of remote sensing haze images demonstrate our approach can achieve better image quality when compared with well-known He's [1] method for remote sensing images.
关键词： atmospheric scattering model,Haze removal,superpixel segmentation
更新于2025-09-04 15:30:14
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Photodetection probability in quantum systems with arbitrarily strong light-matter interaction
摘要： Cavity-QED systems have recently reached a regime where the light-matter interaction strength amounts to a non-negligible fraction of the resonance frequencies of the bare subsystems. In this regime, it is known that the usual normal-order correlation functions for the cavity-photon operators fail to describe both the rate and the statistics of emitted photons. Following Glauber’s original approach, we derive a simple and general quantum theory of photodetection, valid for arbitrary light-matter interaction strengths. Our derivation uses Fermi’s golden rule, together with an expansion of system operators in the eigenbasis of the interacting light-matter system, to arrive at the correct photodetection probabilities. We consider both narrow- and wide-band photodetectors. Our description is also valid for point-like detectors placed inside the optical cavity. As an application, we propose a gedanken experiment confirming the virtual nature of the bare excitations that enrich the ground state of the quantum Rabi model.
关键词： Cavity-QED,light-matter interaction,ultrastrong coupling,quantum Rabi model,photodetection
更新于2025-09-04 15:30:14
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Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms
摘要： Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.
关键词： ensemble model,feature selection,time-series,MODIS and Landsat,remote sensing phenology,heavy metal stress
更新于2025-09-04 15:30:14
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Establishment of two ovarian cancer orthotopic xenograft mouse models for in vivo imaging: A comparative study
摘要： Orthotopic tumor animal models are optimal for preclinical research of novel therapeutic interventions. The aim of the present study was to compare two types of ovarian cancer orthotopic xenograft (OCOX) mouse models, i.e. cellular orthotopic injection (COI) and surgical orthotopic implantation (SOI), regarding xenograft formation rate, in vivo imaging, tumor growth and metastasis, and tumor microenvironment. The tumor formation and progression were monitored by bioluminescent in vivo imaging. Cell proliferation and migration abilities were detected by EdU and scratch assays, respectively. Expression of α-SMA, CD34, MMP2, MMP9, vimentin, E-cadherin and Ki67 in tumor samples were detected by immunohistochemistry. As a result, we successfully established COI- and SOI-OCOX mouse models using ovarian cancer cell lines ES2 and SKOV3. The tumor formation rate in the COI and SOI models were 87.5 and 100%, respectively. Suspected tumor cell leakage occurred in 37.5% of the COI models. The SOI xenografts grew faster, held larger primary tumors, and were more metastatic than the COI xenografts. The migration and proliferation properties of the cells that generated SOI xenografts were significantly starker than those deriving COI xenografts in vitro. The tumor cells in SOI xenografts exhibited a mesenchymal phenotype and proliferated more actively than those in the COI xenografts. Additionally, compared with the COI tumors, the SOI tumors contained more cancer associated fibroblasts, matrix metallopeptidase 2 and 9. In conclusion, SOI is a feasible and reliable technique to establish OCOX mouse models mimicking the clinical process of ovarian cancer growth and metastasis, although SOI is more technically difficult and time-consuming than COI.
关键词： ovarian cancer,orthotopic xenograft model,bioluminescence measurements,metastasis,tumor microenvironment
更新于2025-09-04 15:30:14