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BACKGROUND: Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination. In recent years, the emerging nuclear magnetic resonance imaging (MRI) radiomics can provide a new way for in-depth research on cervical instability. OBJECTIVE: To investigate the application value of MRI radiomics in the study of cervical instability. METHODS: Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital, China Academy of Chinese Medical Sciences, young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection. Five specific regions of interest, including the intervertebral disc region, the facet region, the prevertebral muscle region, the deep region of the posterior cervical muscle group, and the superficial region of the posterior cervical muscle group, were manually segmented to extract and screen the image features. Finally, the cervical instability diagnosis classification model was constructed, and the effectiveness of the model was evaluated using the area under the curve. RESULTS AND CONCLUSION: (1) A total of 56 subjects with cervical instability and 55 subjects with non-instability were included, and 1 688 imaging features were extracted for each region of interest. After screening, 300 sets of specific image feature combinations were obtained, with 60 sets of regions of interest for each group. (2) Five regions of interest diagnostic classification models for cervical instability were initially established. Among them, the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability, and the average area under the curve of ten-fold cross-validation was 0.719 7 and 0.703 3, respectively. (3) The Logistic model in the intervertebral disc region, the LightGBM model in the prevertebral muscle region, and the Logistic model in the superficial posterior cervical muscle region were generally accurate in the classification of instability and non-instability, and the average area under the curve of ten-fold crossvalidation was 0.650 4, 0.620 7, and 0.644 2, respectively. (4) This study proved the feasibility of MRI radiomics in the study of cervical instability, further deepened the understanding of the pathogenesis of cervical instability, and also provided an objective basis for the accurate diagnosis of cervical instability. [ABSTRACT FROM AUTHOR]

Recently, image captioning has become an intriguing task that has attracted many researchers. This paper proposes a novel keypoint-based segmentation algorithm for extracting regions of interest (ROI) and an image captioning model guided by this information to generate more accurate image captions. The Difference of Gaussian (DoG) is used to identify keypoints. A novel ROI segmentation algorithm then utilizes these keypoints to extract the ROI. Features of the ROI are extracted, and the text features of related images are merged into a common semantic space using canonical correlation analysis (CCA) to produce the guiding information. The text features are constructed using a Bag of Words (BoW) model. Based on the guiding information and the entire image features, an LSTM generates a caption for the image. The guiding information helps the LSTM focus on important semantic regions in the image to generate the most significant keywords in the image caption. Experiments on the Flickr8k dataset show that the proposed ROI segmentation algorithm accurately identifies the ROI, and the image captioning model with the guidance information outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: The dynamic regional accuracy of electrical impedance tomography has not yet been validated. We aimed to compare the regional accuracy of electrical impedance tomography with that of four-dimensional computed tomography during dynamic ventilation. Methods: This single-center, prospective, observational study conducted in a general intensive care unit included adult patients receiving mechanical ventilation from July 2021 to February 2024. The patients were mechanically ventilated passively and underwent electrical impedance tomography and four-dimensional computed tomography on the same day. Results: Overall, 45 patients were analyzed. The correlation coefficients in regional dynamic ventilation between four-dimensional computed tomography and electrical impedance tomography in each region were 0.963, 0.963, 0.835 (ventral, central, and dorsal, respectively) in the right lung and 0.947, 0.927, 0.823 (ventral, central, and dorsal, respectively) in the left lung. The correlation coefficient was low when the regional ventilation distribution detected by the electrical impedance tomography was < 2%. After excluding nine patients with a regional ventilation distribution of < 2%, the ventral, central, and dorsal correlation coefficients were 0.963, 0.963, and 0.946 in the right lung and 0.942, 0.924, and 0.951, respectively, in the left lung. Conclusions: Regional ventilation using electrical impedance tomography during dynamic ventilation was highly accurate and consistent with the time phase compared to four-dimensional computed tomography. Given the high correlation between these modalities, they can contribute significantly to further studies on regional ventilation dynamics. Trial registration number ClinicalTrials.gov (No. UMIN00044386). [ABSTRACT FROM AUTHOR]

Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial. Approach. Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1. Main results. The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method. Significance. Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV. [ABSTRACT FROM AUTHOR]

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images. [ABSTRACT FROM AUTHOR]

Background: The impact of excluding intrahepatic segmental vessels from regions of interest (ROIs) on liver stiffness measurement (LSM) via magnetic resonance elastography (MRE) remains uncertain. Purpose: To determine the effect of excluding intrahepatic segmental vessels from ROIs on LSM obtained from MRE. Material and Methods: This retrospective analysis included 95 participants who underwent successful two-dimensional gradient recalled-echo MRE before hepatic tumor resection (n = 49) or living liver donation (n = 46). The conventional LSM was determined by manually drawing ROIs on the elastogram within the 95% confidence region, staying 1 cm within the liver capsule and excluding large hilar vessels, the gallbladder, hepatic lesions, and artifacts. In addition, the modified LSM was determined by excluding intrahepatic segmental vessels. LSMs obtained by the two methods were compared with paired sample signed-rank test. Diagnostic performance for advanced fibrosis was calculated and compared using McNemar's test and Delong's test. The stage of hepatic fibrosis was assessed using surgical specimens by the METAVIR system. Results: The modified LSM was larger than the conventional LSM (2.4 kPa vs. 2.2 kPa in reader 1; 2.7 kPa vs. 2.4 kPa in reader 2; P < 0.001). The modified LSM showed superior sensitivity (0.841 vs. 0.659 in reader 1; 0.864 vs. 0.705 in reader 2; P < 0.05) and area under the curve (0.901 vs. 0.820 in reader 1; 0.912 vs. 0.843 in reader 2; P < 0.05) for detecting advanced fibrosis (≥F3) than conventional LSM. Conclusion: The exclusion of intrahepatic segmental vessels from ROIs in MRE affected the LSM and enhanced diagnostic performance for advanced fibrosis. [ABSTRACT FROM AUTHOR]

The current methods for diagnosing Alzheimer's Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer's affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer's requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer's Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer's Disease. [ABSTRACT FROM AUTHOR]

Due to the high deployment flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have gained a significant attention in civilian and military applications. One of the main essential aspects of UAV is coverage path planning (CPP), which autonomously obtains sufficient paths to cover the entire region of interest (RoI). Several advantages have been offered by UAVs' CPP such as cost and time efficiency, reduced human intervention, resource optimization, data collection, scalability, and adaptability. However, the flight time of UAVs is constrained by battery capacity, necessitating energy-efficient solutions to prolong flight duration. This paper introduces a novel approach for energy-efficient multi-UAV multi-region CPP to generate appropriate paths that cover multiple disjoint regions, aiming to minimize overall energy consumption. First, we employ a back-and-forth strategy to generate intra-region path patterns with minimum turns and propose a smoothing turns approach (STA) based on Bezier curves to effectively reduce an energy consumption due to taking turns. Then, the inter-region path planning is formulated as a multi-constraint optimization problem and solved utilizing the CPLEX solver for small-scale problems and heuristic approaches for large-scale ones. A region allocation approach is proposed to assign RoIs to appropriate UAVs. Simulations are conducted to evaluate the performance of the proposed approach in terms of energy consumption. Comparative results against EECPPA and Nearest Neighbor (NN) approaches demonstrate advantages in energy consumption reduction. Besides, heuristic methods yield superior solutions for large-scale problems within shorter execution times compared to the CPLEX solver. These comparisons highlight the superiority of the proposed approach over existing methods in generating higher-quality solutions. [ABSTRACT FROM AUTHOR]

In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively. [ABSTRACT FROM AUTHOR]

Abstract Background Agitated saline contrast echocardiography, essential for patent foramen ovale detection, typically uses a 10 mL saline protocol, which may lead to operator fatigue and compromise diagnostic accuracy. This study aimed to identify an optimal saline volume that balances diagnostic efficacy and minimizes operator fatigue. Methods Fifty‐three patients with suspected patent foramen ovale underwent echocardiography using three saline protocols: 10 mL (9 mL saline + 1 mL air), 7 mL (6.4 mL saline + 0.6 mL air), and 5 mL (4.6 mL saline + 0.4 mL air). The protocols were evaluated for their impact on microbubble signal intensity in the right atrium, right‐to‐left shunt grading, and operator fatigue, as assessed by the Borg CR10 scale. Results The signal intensities were comparable between the 10 mL and 7 mL protocols, while the 5 mL protocol showed significantly lower signal intensities at all time points (p

Securing image transmissions has become critical as the demand for image sharing and storage grows. In some commercial uses, in order to achieve a balance between encryption security and efficiency, some researchers have tried to encrypt only the region of interest of the user in the image. In order to accurately encrypt region of interest images, this paper proposed a region of interest image encryption algorithm based on lifting scheme and object detection. The algorithm automatically identifies the region of interest in the image and encrypts it securely and effectively. Firstly, the region of interest in the image is detected using the object detection model, and the non-maximum suppression algorithm is modified to solve the problem that the detection box outputted by the object detection model does not completely contain the region of interest. Then the existing pixel extraction method for region of interest images is improved to extract the pixels from the region of interest images at a faster speed, which improves the overall efficiency of the algorithm. Finally, based on the thought of wavelet lifting transform, combined with chaotic system, a two-layer hybrid lifting scheme encryption algorithm is proposed to encrypt the extracted pixels. Experimental results and security analysis show that the algorithm proposed in this paper can effectively protect all objects at once with high security. [ABSTRACT FROM AUTHOR]

The use of diffusion tensor imaging (DTI) has seen significant development over the last two decades, in particular with the development of the tractography of association tracts for preoperative planning of surgery. However, projection tracts are difficult to differentiate from one another and tractography studies have failed to reconstruct these ascending/descending pathways from/to the spinal cord. The present study proposes an atlas of regions of interest (ROIs) designed specifically for projection tracts tractography. Forty‐nine healthy subjects were included in this prospective study. Brain DTI was acquired using the same 3 T MRI scanner, with 32 diffusion directions. Distortions were corrected using the FSL software package. ROIs were drawn using the anterior commissure (AC)–posterior commissure (PC) line on the following landmarks: the pyramid for the corticospinal tract, the medio‐caudal part of the red nucleus for the rubrospinal tract, the pontine reticular nucleus for corticoreticular tract, the superior and inferior cerebellar peduncles for, respectively, the anterior and posterior spinocerebellar tract, the gracilis and cuneatus nucleus for the dorsal columns, and the ventro‐posterolateral nucleus for the spinothalamic tract. Fiber tracking was performed using a deterministic algorithm using DSI Studio software. ROI coordinates, according to AC–PC line, were given for each tract. Tractography was obtained for each tract, allowing tridimensional rendering and comparison of tracking metrics between tracts. The present study reports the accurate design of specific ROIs for tractography of each projection tract. This could be a useful tool in order to differentiate projection tracts at the spinal cord level. [ABSTRACT FROM AUTHOR]

To compare the differences in apparent diffusion coefficient (ADC) and synthetic magnetic resonance (MR) measurements of four region of interest (ROI) placement methods for breast tumor and to investigate their diagnostic performance. 110 (70 malignant, 40 benign) newly diagnosed breast tumors were evaluated. The patients underwent 3.0 T MR examinations including diffusion-weighted imaging and synthetic MR. Two radiologists independently measured ADCs, T 1 relaxation time (T 1), T 2 relaxation time (T 2), and proton density (PD) using four ROI methods: round, square, freehand, and whole-tumor volume (WTV). The interclass correlation coefficient (ICC) was used to assess their measurement reliability. Diagnostic performance was evaluated using multivariate logistic regression analysis and the receiver operating characteristic (ROC) curves. The mean values of all ROI methods showed good or excellent interobserver reproducibility (0.79–0.99) and showed the best diagnostic performance compared to the minimum and maximum values. The square ROI exhibited superior performance in differentiating between benign from malignant breast lesions, followed by the freehand ROI. T 2 , PD, and ADC values were significantly lower in malignant breast lesions compared to benign ones for all ROI methods (p < 0.05). Multiparameters of T 2 + ADC demonstrated the highest AUC values (0.82–0.95), surpassing the diagnostic efficacy of ADC or T 2 alone (p < 0.05). ROI placement significantly influences ADC and synthetic MR values measured in breast tumors. Square ROI and mean values showed superior performance in differentiating benign and malignant breast lesions. The multiparameters of T 2 + ADC surpassed the diagnostic efficacy of a single parameter. [ABSTRACT FROM AUTHOR]

Driver drowsiness and alcohol consumption leads for the majority of road accidents. Intoxicated or drugged drivers are less likely to make timely judgments, react quickly, and operate their vehicles safely. It also affect vision, coordination, and balance, making it difficult to maintain control of the vehicle. To address these primary causes, a real-time, cost-effective, and non-intrusive technology has been developed using powerful deep learning technology. In this research paper a novel Smart AVDNet has been proposed for alcohol detection in the face of car drivers using IMDB-WIKI dataset. The input images are taken from the dashboard camera, the captured images are pre-processed using adaptive unsharp mask guided filter. In the first level of checking, fuzzy based HOG-SSD approach is utilized whether the driver drunk based on the analysis of the eye and mouth. After analysis, drivers face based on the fuzzy decision is send as input to the second level. In this level ROI based CNN techniques by analysing the chin and cheek features of the car drivers. If the driver drunk the car stops automatically and whether the driver is not drunk the camera is keep monitoring. The Smart AVDNet demonstrated an average classification accuracy of 99.39%. When compared with Two-Stage DNN, CNN, and DNN, Smart AVDNet achieved accuracies of 5.39, 17.73, and 0.42%, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. Globally, cardiovascular disease accounts for the majority of deaths, posing a significant health threat. In recent years, LVS has gained important attention due to its ability to measure vital parameters such as myocardial mass, end-diastolic volume, and ejection fraction. Medical professionals realize that manually segmenting data to evaluate these processes takes a lot of time, effort when diagnosing heart diseases. Yet, manually segmenting these images is labour-intensive and may reduce diagnostic accuracy.This paper, propose a combination of different deep neural networks for semantic segmentation of the left ventricle based on Tri-Convolutional Networks (Tri-ConvNets) to obtain highly accurate segmentation. CMRI images are initially pre-processed to remove noise artefacts and enhance image quality, then ROI-based extraction is done in three stages to accurately identify the LV. The extracted features are given as input to three different deep learning structures for segmenting the LV in an efficient way. The contour edges are processed in the standard ConvNet, the contour points are processed using Fully ConvNet and finally the noise free images are converted into patches to perform pixel-wise operations in ConvNets. The proposed Tri-ConvNets model achieves the Jaccard indices of 0.9491 ± 0.0188 for the sunny brook dataset and 0.9497 ± 0.0237 for the York dataset, and the dice index of 0.9419 ± 0.0178 for the ACDC dataset and 0.9414 ± 0.0247 for LVSC dataset respectively. The experimental results also reveal that the proposed Tri-ConvNets model is faster and requires minimal resources compared to state-of-the-art models. [ABSTRACT FROM AUTHOR]

Breast cancer has emerged as one of the most prevalent malignancies affecting women today, underscoring the critical need for advanced diagnostic tools. Mammography has been the conventional method for early breast cancer detection, yet recent years have witnessed the emergence of thermal infrared scans, or thermographies, as a potential contender for diagnosing breast cancer, particularly in cases of dense breast tissue. Thermographic images reveal higher temperatures in regions containing tumors compared to healthy breast tissue, providing a promising avenue for early diagnosis. In parallel, the field of radiography has seen the advent of deep learning (DL) techniques, offering a computational approach to breast cancer identification. This study presents a novel approach, the Enhanced Deep learning-based Convolutional Neural Network (EDCNN), aimed at generating heatmaps from two-dimensional thermal breast images. These heatmaps are employed to quantitatively assess breast vascularity, yielding interpretable parameters for further analysis. In addition, the study proposes a classifier that predicts the likelihood of breast cancer purely based on these extracted parameters. To enhance the accuracy of this process, the algorithm combines Fuzzy C-means clustering with the Region of Interest (ROI) technique to effectively isolate the breast from surrounding body parts. The segmentation results are evaluated using temperature profiles, revealing substantial peaks in the patterns as indicators of ROIs. This identification of hot areas hints at the potential presence of a tumor. To validate the effectiveness of this approach, the study constructs DL models using convolutional neural networks, training them with thermal breast images from the Graphical DMR datasets. The results are compelling, with the EDCNN models outperforming alternative methods, achieving an impressive accuracy of 96.8% and a specificity rate of 93.7%. This research thus offers a robust, efficient, and reliable means of early breast cancer diagnosis, marking a significant advancement in the field. [ABSTRACT FROM AUTHOR]

Objective: There is growing interest in speckle tracking echocardiography‐derived strain as a measure of left ventricular function in neonates. However, knowledge gaps remain regarding the effect of image acquisition and processing parameters on circumferential strain measurements. The aim of this study was to evaluate the effect of using different region of interest (ROI) widths on speckle tracking derived circumferential strain in healthy neonates. Methods: Thirty healthy‐term‐born neonates were examined with speckle‐tracking echocardiography in the short‐axis view. Circumferential strain values were acquired and compared using two different ROI widths. Furthermore, strain values in the different vendor‐defined wall layers were also compared. Results: Increasing ROI width led to a decrease in global circumferential strain (GCS) in the midwall and epicardial layers, the respective decreases in strain being ‐23.4 ±.6% to ‐22.0 ± 1.1%, p <.0001 and 18.5 ± 1.7% to ‐15.6 ± 2.0%, p <.0001. Segmental analyses were consistent with these results, apart from two segments in the midwall. There was no statistically significant effect on strain for the endocardial layer. A gradient was seen where strain increased from the epicardial to endocardial layers. Conclusion: Increasing ROI width led to a decrease in GCS in the midwall and epicardium. There is an increase in circumferential strain when moving from the epicardial toward the endocardial layer. Clinicians wishing to implement circumferential strain into their practice should consider ROI width variation as a potential confounder in their measurements. [ABSTRACT FROM AUTHOR]

Cirrhosis is a liver disease resulting from abnormal continuation of fibrosis, and ultrasound imaging is widely used for cirrhosis diagnosis because of its non‐invasiveness. However, due to unclear appearances of cirrhosis on ultrasound images, diagnoses are difficult and individual results possibly differ depending on the physician's experience. Recently, computer‐aided diagnostic systems using image processing and machine learning have been developed to help physicians detect cirrhosis as a 'Second opinion'. Some related studies have focused on a scenario where physicians set ROIs (Region of Interests) manually because selecting reliable ROIs for training a classifier and classification of patients is indispensable. But, the accuracy of such systems depends inherently on the quality of ROIs, and thus the workloads of physicians increase. In this paper, we propose a reliability evaluation method (REM) for each ROI based on its posterior probability and relationship to peripheral ROIs. The assumption of our proposal is that reliable regions of cirrhosis and normal can be observed in certain regions predominantly. We evaluated the effectiveness of the REM and its optimization for practical use. Experimental results showed that our proposed method curated reliable ROIs and improved classification performance in terms of AUC (Area Under the Curve). © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Background: Discriminating the stage of Graves' ophthalmopathy (GO) is crucial for clinical decision. Application of conventional T2-weighted imaging in the staging is still limited. Purpose: To evaluate the performance of T2 mapping based on two different regions of interest (ROIs) for staging GO. Material and Methods: In total, 56 GO patients were retrospectively enrolled and divided into two groups according to the clinical activity score (CAS). T2 relaxation time (T2RT) of extraocular muscle (EOM) on T2 mapping based on two different ROIs (T2RTROI-1: ROIs were drawn separately in the four EOMs; T2RTROI-2: ROI was drawn in the most inflamed EOM) was measured and compared between active and inactive groups. Results: Both T2RTROI-1 and T2RTROI-2 values in the active GO were significantly higher than those of inactive GO (P <0.001). T2RTROI-1 and T2RTROI-2 values were positively correlated with CAS (rs=0.73, 0.69; P <0.001). When the T2RTROI-1 value of 83.3 ms and T2RTROI-2 value of 106.3 ms were used as cutoff values for staging GO, respectively, the best results were obtained with areas under the curve (AUCs) of 0.822 and 0.827. There was no significant difference for AUCs between T2RTROI-1 and T2RTROI-2 (P = 0.751). Excellent and good inter-observer agreements were achieved in quantitative measurements for T2RTROI-1 and T2RTROI-2 values, respectively, with intraclass correlation coefficients of 0.954 and 0.882. Conclusion: The T2RT values derived from two different ROIs were useful for assessment of disease activity. Taking reproducibility and diagnostic performance into consideration, T2RTROI-1 would be an ideal image biomarker for staging GO compared to T2RTROI-2. [ABSTRACT FROM AUTHOR]

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum. [ABSTRACT FROM AUTHOR]

Most of the current crop row detection algorithms focus on extracting crop canopy rows as location information. However, for some high-pole crops, due to the transverse deviation of the position of the canopy and roots, the agricultural machinery can easily cause the wheel to crush the crop when it is automatically driven. In fact, it is more accurate to use the crop root row as the feature for its location calibration, so a method of crop root row detection is proposed in this paper. Firstly, the ROI (region of interest) of the crop canopy is extracted by a semantic segmentation algorithm, then crop canopy row detection lines are extracted by the horizontal strip division and the midpoint clustering method within the ROI. Next, the Crop Root Representation Learning Model learns the Representation of the crop canopy row and crop root row to obtain the Alignment Equation. Finally, the crop canopy row detection lines are modified according to the Alignment Equation parameters to obtain crop root row detection lines. The average processing time of a single frame image (960 × 540 pix) is 30.49 ms, and the accuracy is 97.1%. The research has important guiding significance for the intelligent navigation, tilling, and fertilization operation of agricultural machinery. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for embedding watermark messages into the crucial part of an image, specifically the region of interest (ROI). The focus is on identifying the human face within an image and embedding the watermark within this detected face. Once the watermark is embedded, the altered face image is reinserted back into the original image. To achieve this, the method employs a face identification algorithm alongside the powerful linear algebra technique known as Singular Value Decomposition (SVD). Additionally, the paper utilizes three widely recognized visual quality metrics to evaluate the method's effectiveness. At the transmitter side, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are used to assess the perceptual quality of the image after watermark embedding. At the receiver side, correlation is measured to test the robustness of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a fractional-order memristor-coupled Hindmarsh–Rose neurons model, which the number and stability of equilibrium points are related to the coupling strength. By Lyapunov exponent spectrum, local attraction basins, spectral entropy and so on, firing pattern transition of the system is revealed. In order to deeply expose information transmission in neural networks, the bifurcation behavior of different neuronal orders is studied by three dimensional two-parameter bifurcation diagram. Furthermore, when external stimulus is applied to a neuron, the system produces anti-monotonicity and bursting behavior. A microcontroller based on ARM is used to implement the system and verify various firing activities. Finally, we use the properties of the chaotic system to design a medical image encryption algorithm based on the region of interest. Numerical simulation results demonstrate that the proposed algorithm can improve the security of medical image transmission and resource utilization. It provides strong resistance against attacks to ensure privacy. [ABSTRACT FROM AUTHOR]

In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis and associated subtype identification majorly rely on the baseline correlation matrix obtained from the functional MRI scan. Thus, the existing protocols are either full of personalized bias or computationally expensive as network complexity-based simple but deterministic protocols are yet to be developed and formalized. This article proposes a deterministic method to identify and differentiate the common ADHD subtypes, which is based on a single complexity measure, namely the eigenvector centrality. The node-wise centrality differences were explored using a classification tree model (p < 0.05) to diagnose the subtypes. Identification of marker nodes from default mode, visual, frontoparietal, limbic, and cerebellar networks strongly vouch for the involvement of multiple brain regions in ADHD neuropathology. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Medical image processing was indispensable to a growing need for quick, effective, and systematic Brain Tumour (BT). Pixels are grouped into larger regions via a process called "region growing" that begins at the seed locations. In noisy images where edges are hard to identify, region growth-related methods perform better than edge-related methods. Dataset taken from Kaggle and URI repository as brain MRI images. The input image undergoes morphological edge detection and the image is then enhanced by reconstructing it through erosion and dilation. In this study, we employ an approach that involves median filtering of the image, Otsu automated segmentation, morphological filtration and dilation, and Improved Whale Optimization - Region based Convolutional Neural Network (IWORCNN) classification. It used the Weka 3.9 tool to perform the classification after preparing the brain Magnetic Resonance Imaging (MRI) database and carrying out the approach in MATLAB R2015a. We compared our approach to the Brain-Surface Extractor (BSE) and a layer-set technique proposed for the mouse brain they analyzed its performance under increasing Signal to Noise Ratio (SNR) and resolution. According to the data, this approach works better than the competition and is reliable at lower resolutions of partial volume impacts and low SNR. The system has a greater accuracy of 98.7%, precision 96.5% and recall 95.6%. [ABSTRACT FROM AUTHOR]

Background: Functional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition. Methods: Thirty-nine post-Covid patients (n = 15 fatigued, n = 24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups. Results: Our results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed. Conclusions: Our study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations.

This research designs a touchless fingerprint identification biometric system. This research problem stems from the user's need to change the conventional system to touchless in the hope of minimizing direct contact that can be scanned by someone who is not interested. This research aims to implement palm images in validating identity using GLCM (Gray-Level Co-occurrence Matrix) features with the K-Nearest Neighbours (K-NN) classification method in MATLAB. The identification process is divided into several stages: image acquisition, pre-processing, feature extraction with GLCM, and database matching using KNN classification. System testing uses the 10-fold cross validation method with 100 image samples (90 training images and 10 test images) that are tested in turn to calculate the average accuracy and analyze system performance. Furthermore, the test used GLCM angles (0o, 45o, 90o dan 135o) and K-NN with k values of 1, 5 and 7. The results showed the highest accuracy of 72% using an angle of 0° in GLCM and k=1 and the lowest at an angle of 90o and k=7 in K-NN. The advantage of this design is the recognition of the identity of the fingerprint owner in real time.

Deep neural networks are susceptible to backdoor attacks during the training stage. When training an image-text retrieval model, if an attacker maliciously injects image-text pairs with a backdoor trigger into the training dataset, the backdoor will be embedded into the model. During the model inference stage, the infected model performs well on benign samples, whereas the secret trigger can activate the hidden backdoor and maliciously change the inference result to the result set by the attacker. The existing researches on backdoor attacks in image-text retrieval are based on the method of directly overlaying the trigger patterns on images, which has the disadvantages of low success rate, obvious abnormal features in poisoned image samples, and low visual concealment. This paper proposes a new backdoor attack method (Diffusion-MUBA) for image-text retrieval models based on diffusion models, designing trigger prompts for the diffusion model. Based on the correspondence between text keywords and regions of interest (ROI) in image-text pair samples, the ROI region in the image samples is edited to generate covert, smooth and natural poisoned training samples, to fine-tune through the pretrained model, establishing incorrect fine-grained word to region alignment in the image-text retrieval model, and embed hidden backdoors into the retrieval model. This paper designs the attack strategy of diffusion model image editing, proposes the backdoor attack model of bidirectional image-text retrieval, and achieves good results in the backdoor attack experiments of image-text retrieval and text-image retrieval. Compared with other backdoor attack methods, it improves the attack success rate, and avoids the impact of introducing specific characteristics of trigger patterns, watermarks, perturbations, local distortions and deformation in the poisoned samples. On this basis, this paper proposes a backdoor attack defense method based on object detection and text matching. It is hoped that the study on the feasibility, concealment, and implementation of backdoor attacks in image and text retrieval may contribute to the development of multimodal backdoor attack defense.

A non-contact-based system for estimating the respiration rate of individuals using a mobile phone camera is presented in this paper. Respiration rate is an important vital sign that indicates the number of breaths a person takes per minute and is commonly used in medical assessments. The method involves recording videos of the pit of the neck region using the mobile phone camera and analyzing the changes in light intensity within the video frames. By processing the obtained signals from the video frames, the system accurately estimates the respiration rate. The effectiveness of the proposed method and the estimated respiration rates are compared with rates obtained using two other methods: a spirometer, which is a device used to measure lung function and manual counting. The evaluation is conducted on a group of 34 participants who are instructed to breathe rapidly to simulate varying respiration rates. The results indicate that the presented method achieves an accuracy of over 80% compared with existing state-of-the-art methods. The standard deviation of ±3% suggests a relatively consistent performance across participants. The calculated error between the estimated respiration rates and the reference rates obtained from the spirometer and manual counting validates the reliability of the proposed approach. Additionally, the paper highlights the advantages of this system, including its cost-effectiveness and suitability for remote monitoring and mass screening. The non-contact nature of the method reduces the need for physical contact with patients, which could be especially beneficial in situations where hygiene and infection control are crucial. The paper also outlines plans for future work, which involve validating the method with larger sample sizes and exploring its potential applications in various clinical scenarios. [ABSTRACT FROM AUTHOR]

Background: The discovery and development of magnetic resonance imaging (MRI) have completely changed how multiple sclerosis (MS) is diagnosed and treated. Image quality has improved as a result of the development of higher Tesla magnets and rapid fluid-attenuated inversion recovery methods. Additionally, new applications have begun to develop, including as three- and four-dimensional methods: functional MRI, magnetic resonance spectroscopy, atrophy measurements, and magnetization transfer ratio imaging. The use of MRI in the context of MS clinical trials has been governed by recently released official guidelines. Additionally, the new McDonald criteria include MRI, allowing for quick diagnosis of MS and timely treatments, and improving patient outcomes. This study attempts to obtain the best image quality for multiple sclerosis patients, who may be affected by some probable artifacts, by examining the diagnostic quality of time repetition (TR) and physical parameters of FLAIR sequences in MRI image. Patient and Methods: The study's design is cross-sectional; the MS patients were under 50 years old when they were identified as having the disease by a neurological clinic and sent for MR examinations. In Wasit City's Alkarama Teaching Hospital, an MRI Philips is being used. FLAIR was the scanning procedure. The TR was scanned twice: once at 6000, and once at 8000. Results: In the setting of MS, the present study's findings demonstrate a statistically significant difference between the mean Region of Interest (RIO) values of the two FLAIR cohorts (6000 and 8000) of 0.002. According to the study, there was no discernible relevance for the RIO mean in any of the different forms of MS. Although there was no significant difference in Contrast to Noise Ratio (CNR) values across the various kinds of multiple sclerosis, there was a significant difference in CNR values between the two FLAIR groups (0.002). Additionally, using the Statistical Package for Social Science (SPSS) application version 26, the data were entered, coded, and analyzed. Conclusion: When the Region of Interest option of the MRI equipment software is used to analyze the MRI signal intensity measurements, the FLAIR imaging approach has proven effective in the investigation of MS in the brain. [ABSTRACT FROM AUTHOR]

Mongolian costumes are one of the representative items of national intangible cultural heritage in China. However it is urgent to improve the research on the cultural system of Mongolian costumes since the original materials of clothing culture are missing gradually and there are fewer occasions for wearing traditional clothing with the continuous changes in the production and life of nomadic peoples. Previous studies focused on three main content types of Mongolian costumes with the first type focusing on the comprehensive analysis of costumes of a single Mongolian tribe the second type attaching importance to the comparative analysis and study of the costumes of multiple Mongolian tribes for specific areas and the third type laying particular emphasis on the systematic study of Mongolian costumes. At present multiple studies focus on costumes of a single tribe such as Erdos Horqin Baerhu Buryat and Ujimqin tribes while there is less or even no research on other tribes and there is a dearth of regional and overall studies. After their interaction with each other during the migration process the costume culture characteristics of each tribe have been integrated into the general characteristics. As research describing the nature of costumes of a single tribe is not enough to reveal the attributes of the overall system it is necessary to carry out systematic analysis and research of Mongolian costumes for in-depth excavation of Mongolian costume culture gene and its evolution which is conducive to the better inheritance and development of the Mongolian costume culture. Eye-moving technology records eye movements through advanced equipment such as eye trackers which can directly reflect the visual movement laws of the subject thereby revealing the psychological cognitive process of the subject and the response to visual elements stimuli. The increasingly maturing eye-moving technology is widely used in various fields such as graphic design product testing scene research and human-computer interaction. It is also widely used in the field of fashion and apparel design as an effective means of Kansei engineering such as overseas clothing function detection brand recognition feature comparison and visual attention mechanism research while domestic applications are mostly used in clothing style feature extraction innovative design and visual attention mechanism research. To reveal the differences in the characteristics of the costumes of the 28 existing Mongolian tribes in Inner Mongolia the article carried out scientific and precise systematic classification research. Firstly according to the local standard DB15 / T 506 - 2012 the 28 samples of typical Mongolian women' s costumes of different tribes were divided into six areas of interest namely headdress collar front fly waist bottoms and sleeves and then the eye-moving experiments were conducted to obtain the indexes of the duration of the first gaze the total duration of gaze the number of times of gaze and heat maps of the 110 experimental participants in the six areas of interest. Finally the software SPSS was used to analyze the significance of differences multivariate ANOVA and cluster analysis of the indicators in the six areas of interest of costumes in the 28 tribes. The article will improve the traditional qualitative method on Mongolian tribal costumes. By using quantitative eye-moving technology to systematically analyze them it achieves more scientific and precise systematic classification research. The results indicate that the main effects of the 28 Mongolian tribal costumes are significant in six areas of interest there is no significant difference between the three major costume categories' regions the ethnicity and gender interactions are significant in front fly waist and sleeve areas of interest and the different areas of interest are significantly affected by different factors and all the tribal costumes can be finally clustered into the three major categories of centralized perception overall perception and headdress perception. To sum up the above research conclusions the eye-moving experiment based on subjects' visual differences are more suitable for the classification study of headdress style costumes patterns trimming process and accessories while the effect of the classification significance study is weaker for waist structure and sleeve process of costumes. In the subsequent research it is necessary to expand the samples of Mongolian tribal costumes in different historical development periods optimize the eye-moving experiment design schemes about costume structure costume patterns trimming and accessories and carry out qualitative and quantitative research and comprehensive analysis to improve the precise classification system of the costumes of the 28 tribes in the Inner Mongolia autonomous region by combining cultural gene theory and digital virtual technology. [ABSTRACT FROM AUTHOR]

An algorithm for detecting partially occluded pedestrians based on multi-feature fusion and tree-structured semi-global stereo matching was proposed to address the issue of reduced detection accuracy in pedestrian detection caused by partial obstruction. The simple linear iterative clustering (SLIC) algorithm was employed for superpixel segmentation to enhance the contour information of pedestrians, and the tree-structured multi-feature fusion semi-global stereo matching algorithm was used to generate depth maps. Pedestrian, background, and obstacle information were separated using an adaptive segmentation algorithm to obtain the region of interest. The region of interest was positioned around the head and shoulders of the pedestrian, where features were distinct and stable, to impose constraints. Feature extraction was conducted using dimension-reduced histogram of gradient (HOG), and a sample set was generated for training an support vector machines(SVM) classifier, ultimately achieving the detection of partially occluded pedestrians. The experiment shows that compared with other pedestrian detection algorithms, the proposed algorithm has a higher accuracy in pedestrian detection in partially occluded scenes, proving the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

'Chronic kidney disease (CKD) – or chronic renal failure (CRF) is a term that encompasses all degrees of decreased kidney function, from damaged–at risk through mild, moderate, and severe chronic kidney failure'. As a risky factor, the disease has steadily turned out to be a major cause of death and morbidity. Accordingly, ultrasound (US) is significant in enhancing the rates of early recognition of CKD. Here, a new CKD detection model is introduced that includes '(1) Pre-processing (2) segmentation (3) Feature extraction, and (4) Classification'. Improved Gaussian filtering is used for pre-processing, and watershed-based segmentation is carried out. Additionally, features like the ROI, mean intensity, and the projected Local Vector Pattern (LVP) are retrieved. The 'Optimized Neural Network (NN) and Long Short-Term Memory (LSTM)' are then provided the output of the features. Additionally, using Self Updated Cat Swarm Optimization, the weights of NN are adjusted in order to increase the classifier's prediction accuracy (SU-CSO). The categorized output is then calculated by averaging the results from the improved NN and LSTM. Lastly, it is demonstrated that the proposed strategy is superior to other options. [ABSTRACT FROM AUTHOR]

Bone age assessment is used to diagnose paediatric growth because some types of bone diseases occur in childhood. To overcome these issues, AlexNet-Based Deep Convolutional Neural Network Optimized with the Group Teaching Optimization Algorithm is proposed. First, input images are gathered via RSNA paediatric bone age dataset. These images are preprocessed using Wavelet Packet Transform Cochlear Filter Bank. Then input hand X-ray images' ROI is segmented using Bayesian fuzzy clustering. Then segmented ROI region is fed to ADCNN that accurately predicts BAA. In general, ADCNN does not divulge any optimization techniques adopted for determining the optimal parameters and ensuring accurate classification. Therefore, the GTOA is used to optimize the ADCNN weight parameters. The proposed approach is done in MATLAB and various performance metrics such as accuracy, F-score, sensitivity, precision, specificity, CCC and CC. The BAA-ADCNN-GTOA method provides higher accuracy 23.75%, 17.97%, 31.65% compared with existing methods, like BAA-CNN-RRNN, BAA-RNN-AF-SFO, BAA-U-Net-CTO- WOA, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Image compression plays a crucial role in the field of medical imaging, including Magnetic Resonance Imaging (MRI). The MRI images are typically large and high-resolution, which results in substantial data storage requirements. Compressing MRI images helps reduce the storage space needed to store the images, making it more efficient and cost-effective to store and transmit them. To overcome these drawbacks, this paper proposes an efficient medical image compression based on hybrid machine learning approaches. There are two main stages are considered in this proposed methodology, named a segmentation stage The Region of Interest (ROI) in the image is recognized by the segmentation stage; and it given to the next stage. Segmentation is carried out by hybrid Grey Wolf Optimization with Fuzzy C-Means (FCM) is proposed to better balance the exploitation and exploration phases of optimization. Then, the neural network i.e., optimized convolutional neural network (Op-CNN), compress the ROI region of the input image depending on the detected segments. Meanwhile, the second region (NROI) is compressed by the Recurrent Neural Networks (RNNs). The suggested method of image compression for medical imaging outcomes and datasets are assessed with highest PSNR value of 45.502, which is higher than the existing techniques. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (CCRCC) comprises 70%–80% of RCCs. The World Health Organization/International Society of Urology Pathology (WHO/ISUP) classification is the most important prognostic factor for CCRCC. By evaluating the variations of tumor microvascular density, contrast-enhanced ultrasound (CEUS) can noninvasively predict the WHO/ISUP grade of CCRCC, and provide the appropriate treatment plan before clinical operation. Methods: In this study, we used CEUS features to analyze 116 CCRCC cases and assess the value of correlation between each indicator and CCRCC WHO/ISUP grading. Results: When compared to high-grade (WHO/ISUP grade III/IV) tumors, low-grade (WHO/ISUP grade I/II) tumors had reduced relative peak intensity (ΔPI) (P = 0.021), relative area under the curve (ΔAUC) (P = 0.019). However, the frequency of incomplete pseudocapsule (P = 0.021) was significantly higher in high-grade tumors. A cut-off value of mean diameter > 5.5 cm, ΔPI > 304 × 10–3, ΔAUC > 350 × 10–3 allowed identification of high-grade tumors with an area under the curve (AUC) of 74.6%, 71.7%, 70.7%, respectively (95% confidence interval). Conclusions: The features of CEUS are effective for differentiating high-grade tumors from low-grade tumors, thus CEUS can be considered an acceptable method for the preoperative assessment of tumor grade. [ABSTRACT FROM AUTHOR]

BackgroundThe design of femoral stem prostheses requires a precise understanding of the femoral marrow cavity. Traditional measurements of morphological parameters in the upper femur, particularly the medullary cavity and cortical region, are primarily based on coronal and sagittal axes, which may not fully capture the true three-dimensional structure of the femur.MethodsPropose a Monte Carlo-based method for a more comprehensive analysis of the femoral marrow cavity, using CT scans of femurs from a selected group of patients. The study aimed to define and calculate anatomically semantic morphological parameters to enhance the understanding of the femoral marrow cavity's anatomical morphological changes, ultimately improving the design and clinical selection of femoral stem prostheses. To enhance the accuracy of femoral stem prosthesis design, this study aims to develop a Monte Carlo-based method for a more comprehensive analysis of the femoral marrow cavity. The proposed method transforms the non-random problem of determining cross-sectional size into a random issue, allowing for the calculation of the size of the medullary cavity and cortical region. Anatomically semantic morphological parameters are then defined, calculated, and analyzed.ResultsThe experimental results indicate that the newly defined parameters complement existing ones, providing a more rational scientific basis for understanding the anatomical morphological changes of the femoral marrow cavity.ConclusionThis research offers essential scientific theoretical support for improved morphologic research, design, and clinical selection of femoral stem prostheses. It holds significant importance and application value in clinical practice, contributing to a more accurate and comprehensive understanding of femoral anatomy for prosthetic design.

Efficient and intuitive representation of floods can improve people’s perception, which is useful for flood emergency management and decision making. However, the current methods of visualizing flood disaster scenes have the shortcomings of data redundancy and low-efficiency. The interference of the complex background can be avoided through region of interest (ROI) extraction, because the attention will be quickly attracted by a few salient visual objects. First, the characteristics of the flood disaster scene object are analysed, and the method for scene division and data organization is established. Second, the region of interest is extracted according to the time series data of the flood evolution process simulated using cellular automata, and the dynamic identification model of the objects of interest is established. Then, a dynamic scheduling queue model with service interruption is designed to optimize the rendering efficiency of flood scenes and improve the perception efficiency. Finally, a prototype visualization system was developed and the experimental results show that approximately 30% of the redundant data are reduced, and the scene rendering efficiency is increased by approximately 15%. The non-ROI visualization is weakened by using the rules of human visual cognition, which improves the perception efficiency of flood scenes.

Due to the fact that invalid cloud-covered regions in remote sensing images consume a considerable quantity of coding bit rates under the limited satellite-to-ground transmission rate, existing image compression methods suffer from low compression efficiency and poor reconstruction quality, especially in cloud-free regions which are generally regarded as regions of interest (ROIs). Therefore, we propose an efficient on-board compression method for remote sensing images with arbitrary-shaped clouds by leveraging the characteristics of cloudy images. Firstly, we introduce two novel spatial preprocessing strategies, namely, the optimized adaptive filling (OAF) strategy and the controllable quantization (CQ) strategy. Specifically, the OAF strategy fills each cloudy region using the contextual information at its inner and outer edge to completely remove the information of cloudy regions and minimize their coding consumption, which is suitable for images with only thick clouds. The CQ strategy implicitly identifies thin and thick clouds and rationally quantifies the data in cloudy regions to alleviate information loss in thin cloud-covered regions, which can achieve the balance between coding efficiency and reconstructed image quality and is more suitable for images containing thin clouds. Secondly, we develop an efficient coding method for a binary cloud mask to effectively save the bit rate of the side information. Our method provides the flexibility for users to choose the desired preprocessing strategy as needed and can be embedded into existing compression framework such as JPEG2000. Experimental results on the GF-1 dataset show that our method effectively reduces the coding consumption of invalid cloud-covered regions and significantly improve the compression efficiency as well as the quality of decoded images.