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In this work, the authors develop a working software-based approach named ‘linearly quantile separated histogram equalisation-grey relational analysis’ for mammogram image (MI). This approach improves overall contrast (local and global) of given MI and segments breast-region with a specific end goal to acquire better visual elucidation, examination, and grouping of mammogram masses to help radiologists in settling on more precise choices. The fundamental commitment of this work is to demonstrate that results of good quality of breast-region segmentation can be accomplished from basic breast-region segmentation if the input image has good contrast and a better interpretation of hidden details. They have evaluated the proposed strategy for MIAS-MIs. Experimental results have shown that the proposed approach works better than state-of-the-art.

Video image-based fire detection technology can overcome some shortcomings of traditional fire detection, and has a good development prospect. This paper summarizes the basic principles of image-based fire detection, and analyzes the main features of fire combustion images. According to these features, firstly, the interframe difference method and the watershed algorithm are used to extract the suspected fire image area which may occur. Then, the features of flame image in early fire stage, such as increasing flame area, fluttering edge, irregular shape and flame color, are used as fire recognition criteria. Meanwhile, various image processing technologies and algorithms are used to extract the four main features of the fire, so as to eliminate various sources of interference and further determine whether a fire has occurred. Finally, a variety of different fuels were selected under indoor conditions to simulate fire experiments under different conditions, and the video was recorded. Fire recognition experiments were carried out using experimental videos and some videos found on the Internet. The experimental results show that the extraction and further recognition of suspected fire areas are both effective. However, the experimental simulation environment is relatively simple, and many theoretical and practical problems need to be further studied and solved.

Evaluating the vulnerability of planar networks, such as road systems, is crucial for optimizing network topology and enhancing network defensive capabilities. In this study, we introduce the concept of “ large-scale region segmentation ” and propose a novel approach for assessing the vulnerability of planar networks. To validate our method, we employ the network efficiency metric to quantify network performance under various attack scenarios, with taking random failures and betweenness-based targeted attacks for comparison. Experiments are conducted on both real-world urban road networks (including Atlanta, Chengdu, Florence, and Paris) and randomly generated planar networks. Results across all tested datasets indicate that the proposed method outperforms both random and betweenness-based attack strategies in rapidly degrading network efficiency. The main contribution of this work lies in considering the overall connectivity of planar networks, rather than focusing solely on individual nodes or edges, thereby introducing a vulnerability analysis approach from a large-scale region segmentation perspective. This approach significantly advances the assessment of planar network vulnerability. Our findings offer practical implications for the planning and emergency management of planar networks, such as transportation systems, and provide a foundational basis for improving network security and efficiency.

Existing visual SLAM systems with neural representations excel in static scenes but fail in dynamic environments where moving objects degrade performance. To address this, we propose a robust dynamic SLAM framework combining classic geometric features for localization with learned photometric features for dense mapping. Our method first tracks objects using instance segmentation and a Kalman filter. We then introduce a cascaded, coarse-to-fine strategy for efficient motion analysis: a lightweight sparse optical flow method performs a coarse screening, while a fine-grained dense optical flow clustering is selectively invoked for ambiguous targets. By filtering features on dynamic regions, our system drastically improves camera pose estimation, reducing Absolute Trajectory Error by up to 95% on dynamic TUM RGB-D sequences compared to ORB-SLAM3, and generates clean dense maps. The 3D Gaussian Splatting backend, optimized with a Gaussian pyramid strategy, ensures high-quality reconstruction. Validations on diverse datasets confirm our system’s robustness, achieving accurate localization and high-fidelity mapping in dynamic scenarios while reducing motion analysis computation by 91.7% over a dense-only approach.

Concurrent nuclear segmentation and classification in Hematoxylin & Eosin-stained histopathology images are a crucial task in disease diagnosis and prognosis. Albeit recent advancement of deep learning models, this task remains challenging as each nucleus occupies a limited number of pixels, and nuclei have large intra-class variability and high inter-class similarities in morphology. In this work, we proposed a tissue region-guided dilated Hover-net (TRG-Dilated Hover-net) that consists of a tissue region segmentation model and a dilated Hover-net model. The latter incorporated the dilated convolution and the atrous spatial pyramid pooling feature pyramids to expand the receptive field; therefore, more information about nuclei and their spacial locations can be captured. Our method achieved the state-of-the-art performance on four benchmark datasets of various cancer types and the in-house curated Breast Cancer dataset.

Medical image encryption is important for maintaining the confidentiality of sensitive medical data and protecting patient privacy. Contemporary healthcare systems store significant patient data in text and graphic form. This research proposes a New 5D hyperchaotic system combined with a customised U-Net architecture. Chaotic maps have become an increasingly popular method for encryption because of their remarkable characteristics, including statistical randomness and sensitivity to initial conditions. The significant region is segmented from the medical images using the U-Net network, and its statistics are utilised as initial conditions to generate the new random sequence. Initially, zig-zag scrambling confuses the pixel position of a medical image and applies further permutation with a new 5D hyperchaotic sequence. Two stages of diffusion are used, such as dynamic DNA flip and dynamic DNA XOR, to enhance the encryption algorithm’s security against various attacks. The randomness of the New 5D hyperchaotic system is verified using the NIST SP800-22 statistical test, calculating the Lyapunov exponent and plotting the attractor diagram of the chaotic sequence. The algorithm validates with statistical measures such as PSNR, MSE, NPCR, UACI, entropy, and Chi-square values. Evaluation is performed for test images yields average horizontal, vertical, and diagonal correlation coefficients of –0.0018, –0.0002, and 0.0007, respectively, Shannon entropy of 7.9971, Kolmogorov Entropy value of 2.9469, NPCR of 99.61%, UACI of 33.49%, Chi-square “PASS” at both the 5% (293.2478) and 1% (310.4574) significance levels, key space is 2 500 and an average encryption time of approximately 2.93 s per 256 × 256 image on a standard desktop CPU. The performance comparisons use various encryption methods and demonstrate that the proposed method ensures secure reliability against various challenges.

Purpose Segmentation of breast lesions in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is critical for effective diagnosis. This study investigates the impact of breast region segmentation (BRS) on the performance of deep learning-based breast lesion segmentation (BLS) in breast DCE-MRI. Methods The study utilized the Stavanger Dataset, comprising 59 DCE-MRI scans, and employed the UNet++ architecture as the segmentation model. Four experimental approaches were designed to assess the influence of BRS on BLS: (1) Whole Volume (WV) without BRS, (2) WV with BRS, (3) BRS applied only to Selected Lesion-containing Slices (SLS), and (4) BRS applied to an Optimal Volume (OV). Data augmentation and oversampling techniques were implemented to address dataset limitations and enhance model generalizability. A systematic method was employed to determine OV sizes for patient’s DCE-MRI images ensuring full lesion inclusion. Model training and validation were conducted using a hybrid loss function—comprising Dice loss, focal loss, and cross-entropy loss—and a five-fold cross-validation strategy. Final evaluations were performed on a randomly split test dataset for each of the four approaches. Results The findings indicate that applying BRS significantly enhances model performance. The most notable improvement was observed in the fourth approach, BRS with OV, which achieved approximately a 50% increase in segmentation accuracy compared to the non-BRS baseline. Furthermore, the BRS with OV approach resulted in a substantial reduction in computational energy consumption—up to 450%, highlighting its potential as an environmentally sustainable solution for large-scale applications.

Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants. Transfer learning of a pre-trained machine learning model was employed for lung region segmentation and image fusion for BPD prediction to enhance the performance of the AI model. The lung segmentation model uses transfer learning to achieve a dice score of 0.960 for preterm infants with ≤ 168 h postnatal age. The BPD prediction model exhibited superior diagnostic performance compared to that of experts and demonstrated consistent performance for chest radiographs obtained at ≤ 24 h postnatal age, and those obtained at 25 to 168 h postnatal age. This study is the first to use deep learning on preterm chest radiographs for lung segmentation to develop a BPD prediction model with an early detection time of less than 24 h. Additionally, this study compared the model’s performance according to both NICHD and Jensen criteria for BPD. Results demonstrate that the AI model surpasses the diagnostic accuracy of experts in predicting lung development in preterm infants.

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance.