Hyperspectral Imaging Using Flexible Endoscopy for Laryngeal Cancer Detection

Based on state projection method with two-step operations, this paper deals with the model reduction problem by analyzing the information descriptions of system states. Our basic idea in obtaining the reduced-order models is to minimize the information loss or the conditional information loss caused by truncation by eliminating the state variables with the least contribution to system information. Before truncation, an entropy preserving transformation of the original state is required. The derived minimum information loss (MIL) and minimum conditional information loss (MCIL) methods are proved to be efficient for approximating stable and unstable systems, respectively, and connected with the balanced truncation methods firmly. Illustrative examples are given.

Advances in hyper spectral imaging sensors makes it possible to obtain high dimensional and high resolution data which poses challenges for computational cost and time. As the inter-band resolution is high in hyper spectral images, the spectral dimension could be reduced without significant loss of useful information. This requires careful application of dimensionality reduction techniques to reduce the dimension but preserve the original spectral characteristics. It is observed in the literature that filtering techniques can address some of the challenges concerning dimensionality reduction in hyper spectral imaging. In this paper a dimensionality reduction method adopting Discrete Wavelet Transform and Distance Classifier Correlation filter has been proposed for hyper spectral imaging. The experimental results show that the combination of ID-DWT with DCCF outperforms the other methods in dimensionality reduction and classification of given data set.

The use of either flexible endoscopy (FE) or rigid endoscopy (RE) for removal of ingested foreign bodies (FBs) impacted in the esophagus is still discussed controversially. We report a consecutive series of 139 patients with FB impaction in the esophagus. During a 6-year period, 69 men and 70 women (median age, 64 [0.7-97] years) requiring removal of an impacted FB underwent either RE (n = 63) in the Otolaryngology Department of our hospital or FE (n = 76) in the Surgical Endoscopy Unit. Foreign body removal was equally effective with FE (success rate 93.4%) and RE (95.2%, p = n.s.). The cases in which foreign body removal failed (5 FE cases [6.6%] and 3 RE cases [4.8%]) were all subsequently successfully managed with "conversion" and use of the other technique. No severe complications occurred when FB removal was attempted with FE (0 of 76 cases; 0.0%), whereas RE was associated with esophageal rupture requiring immediate surgical intervention in 2 of 63 cases (3.2%; p < 0.002). Patient comfort differed significantly between the two procedures (p < 0.0001); RE was always performed under general anesthesia (100.0%), whereas only a minority of patients undergoing FE required general anesthesia (13.0%; p < 0.0001) or mild analgosedation (20.0%). The better patient comfort with FE was also reflected in a significantly lower rate of dysphagia (15%) compared to RE (48%; p < 0.0001). Rigid endoscopy was more frequently used in removal of FBs of the upper esophagus (p < 0.0001), whereas FE was the predominate approach to FBs in the lower esophagus (p < 0.0001). A tailored approach to treatment of FB impaction is recommended. Because of the lower rate of severe complications, better patient comfort with a lower rate of dysphagia, and lack of requirement for general anesthesia, FE should be the "first line" approach to FBs, although RE has its place as the "second line" therapy.

The accurate identification of melon seed varieties is essential for improving seed purity and the overall quality of melon production. In this study, hyperspectral imaging was used to identify six varieties of melon seeds. Both hyperspectral images and RGB images were generated during hyperspectral image acquisition. The spectral features of seeds were extracted from the hyperspectral images. The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. Logistic Regression (LR), Support Vector Classification (SVC), and Extreme Gradient Boosting (XGBoost) were used to establish classification models using spectral features and the manually extracted image features. Convolutional Neural Network (CNN) models were established using the five datasets. The results indicated that the CNN models achieved good performance in all five datasets, with classification accuracies exceeding 90% for the training, validation, and test sets. Also, CNN using the fused datasets obtained optimal performance, achieving classification accuracies exceeding 97% for the training, validation, and test sets. The results indicated that both spectral features and image features can be used to identify the six varieties of melon seeds, and their fusion of spectral features and image features can improve classification performance. These findings provide an alternative approach for melon seed variety identification, which can also be extended to other seed types.

Hyperspectral imaging (HSI) is a novel optical imaging modality, which has recently found diverse applications in the medical field. HSI is a hybrid imaging modality, combining a digital photographic camera with a spectrographic unit, and it allows for a contactless and non-destructive biochemical analysis of living tissue. HSI provides quantitative and qualitative information of the tissue composition at molecular level in a contrast-free manner, hence making it possible to objectively discriminate between different tissue types and between healthy and pathological tissue. Over the last two decades, HSI has been increasingly used in the medical field, and only recently it has found an application in the operating room. In the last few years, several research groups have used this imaging modality as an intraoperative guidance tool within different surgical disciplines. Despite its great potential, HSI still remains far from being routinely used in the daily surgical practice, since it is still largely unknown to most of the surgical community. The aim of this study is to provide clinical surgeons with an overview of the capabilities, current limitations, and future directions of HSI for intraoperative guidance.

This study proposes PlantViT, a Vision Transformer (ViT)-based framework for high-precision vegetation classification by integrating hyperspectral imaging (HSI) and Light Detection and Ranging (LiDAR) data. The dual-branch architecture optimizes feature fusion across spectral and spatial dimensions, where the LiDAR branch extracts elevation and structural features while minimizing information loss and the HSI branch applies involution-based feature extraction to enhance spectral discrimination. By leveraging involution-based feature extraction and a Lightweight ViT (LightViT), the proposed method demonstrates superior classification performance. Experimental results on the Houston 2013 and Trento datasets show that PlantViT achieves an overall accuracy of 99.0% and 97.4%, respectively, with strong agreement indicated by Kappa coefficients of 98.7% and 97.2%. These results highlight PlantViT’s robust capability in classifying heterogeneous vegetation, outperforming conventional CNN-based and other ViT-based models. This study advances Unmanned Aerial Vehicle (UAV)-based remote sensing (RS) for environmental monitoring by providing a scalable and efficient solution for wetland and forest ecosystem assessment.

This work was supported in part by the National Science Fund for Excellent Young Scholars under Grant 61522107, by the Natural Science Foundation of China under Grant 61371180, by the China Aerospace Science and Technology Corporation–Harbin Institute of Technology Joint Center for Technology Innovation Fund under Grant CASC-HIT15-1C03, and by the Smart Sea Technical Innovation Foundation, CSSC-SERI.

Hyperspectral imaging is an optical method that provides a large amount of information about the investigated object. Its medical applications are reviewed in this article, including tumor delimitation and identification, assessing tissue perfusion and its pathological conditions (including some complications like diabetic foot ulceration), making accurate surgical decisions, evaluating the health of dental structures, etc. Many of the articles show very promising results that required brief comments by the authors. It is clear that choosing the appropriate hyperspectral imaging system for each medical field, together with the most reliable hyperspectral image processing methods, are the main goals of future studies, before hyperspectral imaging becomes a widely applicable evaluation method in medicine. The authors try to answer some questions on this topic and set up some directions for future research.

Optical Wireless Communication [OWC] involves communication over a light media by a Transmit-Receive pair. In Optical Camera communication (OCC), a camera is involved in decoding the received information while transmitter transmits the information by encoding it in a spatially arranged colour codes. If there is information loss in the received image resulting in the incorrect decoding of the received information, the overall system throughput will decrease. Information loss could be due to long distance, low camera resolution, background illumination and opaqueness of the media between the transmitter and the receiver. This paper presents a rate adaptation mechanism to improve the communication throughput by minimizing information loss. Based on our literature survey, we find that our approach is novel. We have also presented a comparative performance with and without rate adaptation. While throughput improvement with rate adaptation varies in our experiment, the maximum improvement we have achieved is by ∼50%.

With the growing technology and amount of data, the use of cloud and centralized servers is increasing. When the data of the individuals is stored by the third party it leads to the insecurity of misuse of their information in the individuals. Data Mining is carried on this data stored over cloud to get useful information, analysis and decision making purposes. But along with the process of mining, privacy preservation is the key concern. While performing data mining over large set of data there are chances of privacy violation, loss of information, loss of privacy of individuals. While concentrating only on preserving privacy can increase inefficiency and complexity of data mining techniques. Techniques used for achieving privacy lead to a significant information loss. The methods concerned about information loss generally lack behind in achieving a good level of privacy. Thus it is important to find the efficient data mining technique which can preserve privacy without causing high loss of information. This paper analyses various privacy preserving data mining techniques for their pros and cons and then gives a proposed concept to achieve privacy with minimum information loss.

With the abundant spectral information, the hyperspectral images could be benefit for tracking the target in various application scenarios. Most of the predominant hyperspectral object tracking methods were based on transferred red–green–blue (RGB) object tracking networks, since the lack of training data. Different strategies of processing the hyperspectral images to adapt the transferred RGB object tracking networks have been exploited. However, the existing strategies led to lose the spectral information or the interaction information between bands and have shown limited performances. In this article, a novel transformer-based hyperspectral object tracking algorithm (Trans-HST) is proposed to make advantages of the spectral information with transformer modules. In Trans-HST, the cross-band groups of feature enhancement (CBFE) is introduced to reduce the negative effects of the interaction information loss. To address the problem of spectral information loss, the transformer-based deep features’ fusion (TDFF) fuses the deep features corresponding to different groups of bands in the hyperspectral images and integrates the deep feature corresponding to the original hyperspectral images into the fused features. Experiments on the commonly used hyperspectral object tracking dataset have been applied to verify the effectiveness of the two proposed modules, and they indicate the superior performance of the Trans-HST comparing with other RGB and hyperspectral trackers.

Speckle noise, being multiplicative, degrades the quality of ultrasound images, making it very challenging to achieve successful diagnoses, particularly of PCOS, where ovarian cyst detection is crucial. Its nature, usually characterized by the Gamma distribution, makes it difficult to remove the noise without inducing distortion in the signal, resulting in loss of information and decreased diagnostic specificity. There is an inherent trade-off between the extent of noise suppression and the preservation of image details; therefore, this study aims to reduce speckle noise to the maximum extent possible while retaining critical diagnostic information. This paper proposes a novel framework to systematically determine and fine-tune the smoothing control parameters (SCPs) of five diagnostically important despeckling methods, particularly for ovarian ultrasound images. Experiments were performed on public and clinically acquired ovarian ultrasound datasets to evaluate the generalizability of the optimized SCPs. Among the applied speckle reduction techniques, the optimized Kuan filter with 512 NLOOKs yielded the best overall performance. It provided SSIM values between 0.91 and 1.00 ( -0.04), PSNR values between 74 and ( -2.68), and minimum MSE values of 0.00-0.01 ( ), indicating excellent speckle suppression and strong preservation of anatomical and textural information in ovarian ultrasound images. The Frost filter, tuned with a damping factor of 5, achieved comparably strong performance, maintaining an SSIM of , PSNR values of 60- ( -2.66), and MSE values of 0.01-0.07 ( -0.04). These metrics highlight its ability to balance noise reduction with effective structural preservation. One-way ANOVA supported the superiority of these two filters, showing very high F-values and correspondingly low p-values, indicating highly significant performance differences ( ). Collectively, the optimized SCPs provide a reliable and generalizable strategy for improving ultrasound image quality across diverse datasets.

In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image classification and have achieved good performance. However, the high dimensions and few samples of hyperspectral remote sensing images tend to be the main factors restricting improvements in classification performance. At present, most advanced classification methods are based on the joint extraction of spatial and spectral features. In this article, an improved dense block based on a multiscale spectral pyramid (MSSP) is proposed. This method uses the idea of multiscale and group convolution of the convolution kernel, which can fully extract spectral information from hyperspectral images. The designed MSSP is the main unit of the spectral dense block (called MSSP Block). Additionally, a short connection with nonlinear transformation is introduced to enhance the representation ability of the model. To demonstrate the effectiveness of the proposed dual-branch multiscale spectral attention network, some experiments are conducted on five commonly used datasets. The experimental results show that, compared with some state-of-the-art methods, the proposed method can provide better classification performance and has strong generalization ability.

A criterion is presented to indicate a strategy for additively combining corresponding discrete rows and columns of a square array to form an array of reduced size such that the loss of information thereby incurred is minimized. The objective function is expressed in terms of an information loss criterion between the original array and an array of the same size acting as a surrogate for the reduced array. In contrast to the usual application of information theory in planning, where arrays are filled out based on incomplete information, the converse case is implied here, in which a particular partial representation of the array is sought (that is, the aggregated array) which most closely approaches the information content of the original full array. The method is expected to be useful for situations where available large arrays of data need to be appropriately reduced in size so that they can be efficiently used in further computations. Alternatively, the original array may contain current observed data, and the primary aim may be to determine from this array the aggregation scheme to use in future forecasting calculations. Typical applications may include the aggregation of spatial zones, the aggregation of economic sectors in input-output analysis, as well as various clustering problems.

Even in a scenario where Big Data has increasingly become a part of company structure, it is not uncommon to come across statistical or mathematical analysis processes lacking sufficient available observations to consider all the business variables of interest. As a consequence, it affects, in different levels, all analytic methods whether statistical or mathematical. Specifically in the case of the benchmarking model known as DEA (Data Envelopment Analysis), the convergence of involved algorithms is directly dependent on the relation between the quantity of observations and the quantity of variable considered. Any attempt to circumvent the conditions imposed by these methodologies will result in loss of information. The scope of this study is to propose an alternative method to deal with this problem when the DEA model is applied and minimize information loss.