Journal of Imaging最新文献

High-speed realization of computer-generated holograms (CGHs) is a crucial problem in the field of modern 3D visualization and optical image processing system development. Binary CGHs can be realized using high-resolution, high-speed spatial light modulators such as ferroelectric liquid crystals on silicon devices or digital micro-mirror devices providing the high throughput of optoelectronic systems. However, the quality of holographic images restored by binary CGHs often suffers from distortions, background noise, and speckle noise caused by the limitations and imperfections of optical system components. The present manuscript introduces a method based on the optimization of CGH models directly in the optical system with a camera-in-the-loop configuration using effective direct search with a random trajectory algorithm. The method was experimentally verified. The results demonstrate a significant enhancement in the quality of the holographic images optically restored by binary-phase CGH models optimized through this method compared to purely digitally generated models.

Accurate segmentation and classification of kidney pathologies from medical images remain a major challenge in computer-aided diagnosis due to complex morphological variations, small lesion sizes, and severe class imbalance. This study introduces DiagNeXt, a novel two-stage deep learning framework designed to overcome these challenges through an integrated use of attention-enhanced ConvNeXt architectures for both segmentation and classification. In the first stage, DiagNeXt-Seg employs a U-Net-based design incorporating Enhanced Convolutional Blocks (ECBs) with spatial attention gates and Atrous Spatial Pyramid Pooling (ASPP) to achieve precise multi-class kidney segmentation. In the second stage, DiagNeXt-Cls utilizes the segmented regions of interest (ROIs) for pathology classification through a hierarchical multi-resolution strategy enhanced by Context-Aware Feature Fusion (CAFF) and Evidential Deep Learning (EDL) for uncertainty estimation. The main contributions of this work include: (1) enhanced ConvNeXt blocks with large-kernel depthwise convolutions optimized for 3D medical imaging, (2) a boundary-aware compound loss combining Dice, cross-entropy, focal, and distance transform terms to improve segmentation precision, (3) attention-guided skip connections preserving fine-grained spatial details, (4) hierarchical multi-scale feature modeling for robust pathology recognition, and (5) a confidence-modulated classification approach integrating segmentation quality metrics for reliable decision-making. Extensive experiments on a large kidney CT dataset comprising 3847 patients demonstrate that DiagNeXt achieves 98.9% classification accuracy, outperforming state-of-the-art approaches by 6.8%. The framework attains near-perfect AUC scores across all pathology classes (Normal: 1.000, Tumor: 1.000, Cyst: 0.999, Stone: 0.994) while offering clinically interpretable uncertainty maps and attention visualizations. The superior diagnostic accuracy, computational efficiency (6.2× faster inference), and interpretability of DiagNeXt make it a strong candidate for real-world integration into clinical kidney disease diagnosis and treatment planning systems.

Registering UAV-based thermal and visible images is a challenging task due to differences in appearance across spectra and the lack of public benchmarks. To address this issue, we introduce UAV-TIRVis, a dataset consisting of 80 accurately and manually registered UAV-based thermal (640 × 512) and visible (4K) image pairs, captured across diverse environments. We benchmark our dataset using well-known registration methods, including feature-based (ORB, SURF, SIFT, KAZE), correlation-based, and intensity-based methods, as well as a custom, heuristic intensity-based method. We evaluate the performance of these methods using four metrics: RMSE, PSNR, SSIM, and NCC, averaged per scenario and across the entire dataset. The results show that conventional methods often fail to generalize across scenes, yielding <0.6 NCC on average, whereas the heuristic method shows that it is possible to achieve 0.77 SSIM and 0.82 NCC, highlighting the difficulty of cross-spectral UAV alignment and the need for further research to improve optimization in existing registration methods.

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The internal quality assessment of potato tubers is a crucial task in agro-laboratory processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in laboratory. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 95% and precision near 97.2% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.

Text-to-image generative models can be used to imitate historical artistic styles, but their effectiveness in doing so remains unclear. In this work, we propose an evaluation framework that leverages expert knowledge from art history and visual semiotics and combines it with quantitative analysis to assess stylistic fidelity. Three experts rated both historical artwork production and images generated with Midjourney v6 for five major movements (Abstract Art, Cubism, Expressionism, Impressionism, Surrealism) and ten associated painters (male and female pairs), using nine visual criteria grounded in Greimas's plastic categories and Wölfflin's stylistic oppositions. Ratings were expressed as 95% intervals on continuous 0-100 scales and compared using our Relative Ratings Map (RRMap), which summarizes relative shifts, relative dispersion, and distributional overlap (via the Bhattacharyya coefficient). They were also discretized in four quality ratings (bad, stereotype, fair, excellent). The results show strong inter-expert variability and more moderate intra-expert effects tied to movements, criteria, criterion groups and modalities. Experts tend to agree that the model sometimes aligns with historical trends but also sometimes produces stereotyped versions of a movement or painter, or even completely missed its target, although no unanimous consensus emerges. We conclude that evaluating generative models requires both expert-driven interpretation and quantitative tools, and that stylistic fidelity is hard to quantify even with a rigorous framework.

Oxygen-ozone (O₂-O₃) therapy is widely used for treating lumbar disc herniation. However, controversy remains regarding the safest and most effective route of administration. While intradiscal injection is purported to show clinical efficacy, it has also been associated with serious complications. In contrast, the intramuscular route can exhibit a more favourable safety profile and comparable pain outcomes, suggesting its potential as a safer alternative in selected patient populations. This mixed-method study combined computed tomography (CT) imaging, biophysical diffusion modelling, and a meta-analysis of clinical trials to evaluate whether intramuscular O₂-O₃ therapy can achieve disc penetration and therapeutic efficacy comparable to intradiscal nucleolysis, while minimizing procedural risk. Literature searches across PubMed, Scopus, and Cochrane databases identified seven eligible studies (four randomized controlled trials and three cohort studies), encompassing a total of 120 patients. Statistical analyses included Hedges' g, odds ratios, and number needed to harm (NNH). CT imaging demonstrated gas migration into the intervertebral disc within minutes after intramuscular injection, confirming the plausibility of diffusion through annular micro-fissures. The meta-analysis revealed substantial pain reduction with intramuscular therapy (Hedges' g = -1.55) and very high efficacy with intradiscal treatment (g = 2.87), though the latter was associated with significantly greater heterogeneity and higher complication rates. The relative risk of severe adverse events was 6.57 times higher for intradiscal procedures (NNH ≈ 1180). O₂-O₃ therapy offers a biologically plausible, safer, and effective alternative to intradiscal injection, supporting its adoption as a first-line, minimally invasive strategy for managing lumbar disc herniation.

Precise classification of brain tumors is crucial for early diagnosis and treatment, but obtaining tumor masks is extremely challenging, limiting the application of traditional methods. This paper proposes a brain tumor classification model based on whole-brain images, combining a spatial block-residual block cooperative architecture with striped pooling feature fusion to achieve multi-scale feature representation without requiring tumor masks. The model extracts fine-grained morphological features through three shallow VGG spatial blocks while capturing global contextual information between tumors and surrounding tissues via four deep ResNet residual blocks. Residual connections mitigate gradient vanishing. To effectively fuse multi-level features, strip pooling modules are introduced after the third spatial block and fourth residual block, enabling cross-layer feature integration-particularly optimizing representation of irregular tumor regions. The fused features undergo cross-scale concatenation, integrating both spatial perception and semantic information, and are ultimately classified via an end-to-end Softmax classifier. Experimental results demonstrate that the model achieves an accuracy of 97.29% in brain tumor image classification tasks, significantly outperforming traditional convolutional neural networks. This validates its effectiveness in achieving high-precision, multi-scale feature learning and classification without brain tumor masks, holding potential clinical application value.

Background: The early and accurate classification of ischemic stroke stages on computed tomography (CT) remains challenging due to subtle attenuation differences and significant scanner variability. This study developed a neural network framework to dynamically optimize Hounsfield Unit (HU) transformations and CLAHE parameters for temporal stage-specific stroke classification.

Methods: We analyzed 1480 CT cases from 68 patients across five stages (hyperacute, acute, subacute, chronic, and normal). The training data were augmented via horizontal flipping, ±7° rotation. A convolutional neural network (CNN) was used to optimize linear transformation and CLAHE parameters through a combined loss function incorporating the effective measure of enhancement (EME), peak signal-to-noise ratio (PSNR), and regularization. the enhanced images were classified using logistic regression (LR), support vector machines (SVMs), and random forests (RFs) with 25-fold cross-validation. Model interpretability was evaluated using Grad-CAM.

Results: Neural network optimization significantly outperformed static parameters across validation metrics. Deep CLAHE achieved the following accuracies versus static CLAHE: hyperacute (0.9838 vs. 0.9754), acute (0.9904 vs. 0.9873), subacute (0.9948 vs. 0.9825), and chronic (near-perfect 0.9979 vs. 0.9808). Qualitative interpretability analysis confirmed that models focused on clinically relevant regions, with optimized enhancement producing more coherent attention patterns than static methods. Parameter analysis revealed stage-aware adaptation: conservative enhancement in early phases (slope: 1.249-1.257), maximized in subacute (slope: 1.290-1.292), and restrained in the chronic phase (slope: 1.240-1.258), reflecting underlying stroke pathophysiology.

Conclusions: A neural network-optimized framework with interpretability validation provides stage-specific stroke classification that achieves superior performance over static methods. Its pathophysiology-aligned parameter adaptation offers a clinically viable and transparent solution for emergency stroke assessment.

In recent years, deep learning networks have demonstrated remarkable progress in the semantic segmentation of historical documents. Nonetheless, their limited explainability remains a critical concern, as these models frequently operate as black boxes, thereby constraining confidence in the trustworthiness of their outputs. To enhance transparency and reliability in their deployment, increasing attention has been directed toward explainable artificial intelligence (XAI) techniques. These techniques typically produce fine-grained attribution maps in the form of heatmaps, illustrating feature contributions from different blocks and layers within a deep neural network (DNN). However, such maps often closely resemble the segmentation outputs themselves, and there is currently no consensus regarding appropriate explainability metrics for semantic segmentation. To overcome these challenges, we present SegClarity, a novel workflow designed to integrate explainability into the analysis of historical documents. The workflow combines visual and quantitative evaluations specifically tailored to segmentation-based applications. Furthermore, we introduce the Attribution Concordance Score (ACS), a new explainability metric that provides quantitative insights into the consistency and reliability of attribution maps. To evaluate the effectiveness of our approach, we conducted extensive qualitative and quantitative experiments using two datasets of historical document images, two U-Net model variants, and four attribution-based XAI methods. A qualitative assessment involved four XAI methods across multiple U-Net layers, including comparisons at the input level with state-of-the-art perturbation methods RISE and MiSuRe. Quantitatively, five XAI evaluation metrics were employed to benchmark these approaches comprehensively. Beyond historical document analysis, we further validated the workflow's generalization by demonstrating its transferability to the Cityscapes dataset, a challenging benchmark for urban scene segmentation. The results demonstrate that the proposed workflow substantially improves the interpretability and reliability of deep learning models applied to the semantic segmentation of historical documents. To enhance reproducibility, we have released SegClarity's source code along with interactive examples of the proposed workflow.