Journal of Imaging最新文献_第2页

Novel, Contrast Echocardiography-Based Trabeculation Quantification Method in the Diagnosis of Left Ventricular Excessive Trabeculation. 基于超声造影的左室过度小梁定量诊断新方法。

IF 2.7

Journal of Imaging Pub Date : 2026-04-14 DOI: 10.3390/jimaging12040169

Kristóf Attila Farkas-Sütő, Balázs Mester, Flóra Klára Gyulánczi, Krisztina Filipkó, Hajnalka Vágó, Béla Merkely, Andrea Szűcs

{"title":"Novel, Contrast Echocardiography-Based Trabeculation Quantification Method in the Diagnosis of Left Ventricular Excessive Trabeculation.","authors":"Kristóf Attila Farkas-Sütő, Balázs Mester, Flóra Klára Gyulánczi, Krisztina Filipkó, Hajnalka Vágó, Béla Merkely, Andrea Szűcs","doi":"10.3390/jimaging12040169","DOIUrl":"https://doi.org/10.3390/jimaging12040169","url":null,"abstract":"Cardiac MRI (CMR) is the gold standard for diagnosing left ventricular excessive trabeculation (LVET), whereas echocardiography (Echo) often does not yield a definitive diagnosis. The use of ultrasound contrast material offers the potential for more accurate imaging of the trabecular system; however, we do not yet have diagnostic criteria developed specifically for contrast Echo (CE-Echo). We aimed to determine the role of CE-Echo in the diagnosis of LVET and to propose a novel method for quantifying trabeculation. We included 55 LVET subjects and 54 age- and sex-matched healthy Control subjects. All subjects underwent non-contrast Echo, CE-Echo, and CMR examinations. In addition to volumetric parameters and ejection fraction (EF), we measured the area of the trabeculated layer and its ratio to the LV area (Trab/LV_area) on apical CE-Echo views. Based on the CMR-derived diagnosis, the Trab/LV_area ratio identified individuals with LVET with high specificity (98%) and sensitivity (95%) when the average of the apical views reached 17% (AUC = 0.98), or when it exceeded 20% in at least one view (AUC = 0.96). The use of CE-Echo may assist in the quantitative diagnosis of LVET in addition to its morphological assessment, and the Trab_area/LVarea may be a good additional criterion in the diagnosis of LVET.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117673/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing CNNs and LoRA-Fine-Tuned Vision-Language Models for Breast Cancer Histopathology Image Classification. 评估cnn和lora微调视觉语言模型在乳腺癌组织病理学图像分类中的应用。

IF 2.7

Journal of Imaging Pub Date : 2026-04-14 DOI: 10.3390/jimaging12040168

Tomiris M Zhaksylyk, Beibit B Abdikenov, Nurbek M Saidnassim, Birzhan T Ayanbayev, Aruzhan S Imasheva, Temirlan S Karibekov

{"title":"Assessing CNNs and LoRA-Fine-Tuned Vision-Language Models for Breast Cancer Histopathology Image Classification.","authors":"Tomiris M Zhaksylyk, Beibit B Abdikenov, Nurbek M Saidnassim, Birzhan T Ayanbayev, Aruzhan S Imasheva, Temirlan S Karibekov","doi":"10.3390/jimaging12040168","DOIUrl":"https://doi.org/10.3390/jimaging12040168","url":null,"abstract":"Breast cancer histopathology classification remains a fundamental challenge in computational pathology due to variations in tissue morphology across magnification levels. Convolutional neural networks (CNNs) have long been the standard for image-based diagnosis, yet recent advances in vision-language models (VLMs) suggest they may provide strong and transferable representations for complex medical images. In this study, we present a systematic comparison between CNN baselines and large VLMs-Qwen2 and SmolVLM-fine-tuned with Low-Rank Adaptation (LoRA; r=16, α=32, dropout = 0.05) on the BreakHis dataset. Models were evaluated at 40×, 100×, 200×, and 400× magnifications using accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). While Qwen2 achieved moderate performance across magnifications (e.g., 0.8736 accuracy and 0.9552 AUC at 200×), SmolVLM consistently outperformed Qwen2 and substantially reduced the gap with CNN baselines, reaching up to 0.9453 accuracy and 0.9572 F1-score at 200×-approaching the performance of AlexNet (0.9543 accuracy) at the same magnification. CNN baselines, particularly ResNet34, remained the strongest models overall, achieving the highest performance across all magnifications (e.g., 0.9879 accuracy and 0.9984 AUC at 40×). These findings demonstrate that LoRA fine-tuned VLMs, despite requiring gradient accumulation and memory-efficient optimizers and operating with a significantly smaller number of trainable parameters, can achieve competitive performance relative to traditional CNNs. However, CNN-based architectures still provide the highest accuracy and robustness for histopathology classification. Our results highlight the potential of VLMs as parameter-efficient alternatives for digital pathology tasks, particularly in resource-constrained settings.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions. 人工智能在肺内窥镜检查中的应用：目前的证据、局限性和未来的方向。

IF 2.7

Journal of Imaging Pub Date : 2026-04-12 DOI: 10.3390/jimaging12040167

Sara Lopes, Miguel Mascarenhas, João Fonseca, Adelino F Leite-Moreira

{"title":"Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.","authors":"Sara Lopes, Miguel Mascarenhas, João Fonseca, Adelino F Leite-Moreira","doi":"10.3390/jimaging12040167","DOIUrl":"https://doi.org/10.3390/jimaging12040167","url":null,"abstract":"Background: Artificial intelligence (AI) is increasingly applied in pulmonary endoscopy, including diagnostic bronchoscopy, interventional pulmonology and endobronchial imaging. Advances in computer vision, machine learning and robotic systems have expanded the potential for automated lesion detection, navigation to peripheral pulmonary lesions, and real-time procedural support. However, the current evidence base remains heterogeneous, and translational challenges persist.Methods: This review summarizes current applications and developments of AI across white-light bronchoscopy (WLB), image-enhanced bronchoscopy (e.g., narrow-band imaging and autofluorescence imaging), endobronchial ultrasound (EBUS), virtual and robotic bronchoscopies, and workflow optimization and training. The authors also examine the methodological limitations, regulatory considerations, and implementation barriers that affect translation into routine practice.Results: Reported developments include deep learning-based models for mucosal abnormality detection, lymph-node characterization during EBUS-guided transbronchial needle aspiration (EBUS-TBNA), improved lesion localization, and reduction in operator-dependent variability. Additionally, AI-assisted simulation platforms and decision-support tools are reshaping training paradigms. Nevertheless, most studies remain retrospective or single-center, with limited external validation, dataset heterogeneity, unclear model explainability, and incomplete integration into clinical workflows.Conclusions: AI has the potential to support lesion detection, navigation, and training in pulmonary endoscopy. However, robust prospective validation, standardized datasets, transparent model reporting, robust data governance, multidisciplinary collaboration, and careful integration into clinical practice are required before widespread adoption.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Probabilistic Short-Term Sky Image Forecasting Using VQ-VAE and Transformer Models on Sky Camera Data. 基于VQ-VAE和Transformer模型的天象短期概率预报。

IF 2.7

Journal of Imaging Pub Date : 2026-04-10 DOI: 10.3390/jimaging12040165

Chingiz Seyidbayli, Soheil Nezakat, Andreas Reinhardt

{"title":"Probabilistic Short-Term Sky Image Forecasting Using VQ-VAE and Transformer Models on Sky Camera Data.","authors":"Chingiz Seyidbayli, Soheil Nezakat, Andreas Reinhardt","doi":"10.3390/jimaging12040165","DOIUrl":"https://doi.org/10.3390/jimaging12040165","url":null,"abstract":"Cloud cover significantly reduces the electrical power output of photovoltaic systems, making accurate short-term cloud movement predictions essential for reliable solar energy production planning. This article presents a deep learning framework that directly estimates cloud movement from ground-based all-sky camera images, rather than predicting future production from past power data. The system is based on a three-step process: First, a lightweight Convolutional Neural Network segments cloud regions and produces probabilistic masks that represent the spatial distribution of clouds in a compact and computationally efficient manner. This allows subsequent models to focus on the geometry of clouds rather than irrelevant visual features such as illumination changes. Second, a Vector Quantized Variational Autoencoder compresses these masks into discrete latent token sequences, reducing dimensionality while preserving fundamental cloud structure patterns. Third, a GPT-style autoregressive transformer learns temporal dependencies in this token space and predicts future sequences based on past observations, enabling iterative multi-step predictions, where each prediction serves as the input for subsequent time steps. Our evaluations show an average intersection-over-union ratio of 0.92 and a pixel accuracy of 0.96 for single-step (5 s ahead) predictions, while performance smoothly decreases to an intersection-over-union ratio of 0.65 and an accuracy of 0.80 in 10 min autoregressive propagation. The framework also provides prediction uncertainty estimates through token-level entropy measurement, which shows positive correlation with prediction error and serves as a confidence indicator for downstream decision-making in solar energy forecasting applications.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Resolution Measurement of Surface Normal Maps Using Specular Reflection Imaging. 使用镜面反射成像的表面法线贴图的高分辨率测量。

IF 2.7

Journal of Imaging Pub Date : 2026-04-10 DOI: 10.3390/jimaging12040164

Shinichi Inoue, Yoshinori Igarashi, Seiji Suzuki

{"title":"High-Resolution Measurement of Surface Normal Maps Using Specular Reflection Imaging.","authors":"Shinichi Inoue, Yoshinori Igarashi, Seiji Suzuki","doi":"10.3390/jimaging12040164","DOIUrl":"https://doi.org/10.3390/jimaging12040164","url":null,"abstract":"This paper presents a method for measuring the spatial distribution of surface normal vectors with high angular accuracy. The measured data are visualized using a color-mapping technique and represented as normal maps, which are commonly used in computer graphics. Reliable methods for evaluating material surface properties have long been sought in industrial applications where visual assessments of reflective properties are still widely employed, particularly in appearance-critical fields. Motivated by this need, we introduce an imaging-based technique for measuring the high-resolution spatial distribution of surface normal vectors from specular reflection. A dedicated measurement apparatus was developed to capture surface normal vectors at 1024 × 1024 sampling points with a spatial resolution of 0.02 × 0.02 mm and an angular resolution of approximately 0.1°. Using this apparatus, normal maps were obtained for various materials, including plastic, ceramic tile, inkjet paper, and aluminum sheets. The spatial distribution of surface normal vectors reflects surface roughness, which strongly influences perceived texture. The resulting normal maps enable not only quantitative surface analysis for industrial inspection but also the physical reproduction of gloss in computer graphics.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A TV-BM3D Iterative Algorithm for VMAT-CT Reconstruction. VMAT-CT重建的TV-BM3D迭代算法。

IF 2.7

Journal of Imaging Pub Date : 2026-04-10 DOI: 10.3390/jimaging12040166

Chia-Lung Chien, Beibei Guo, Rui Zhang

{"title":"A TV-BM3D Iterative Algorithm for VMAT-CT Reconstruction.","authors":"Chia-Lung Chien, Beibei Guo, Rui Zhang","doi":"10.3390/jimaging12040166","DOIUrl":"https://doi.org/10.3390/jimaging12040166","url":null,"abstract":"Volumetric modulated arc therapy-computed tomography (VMAT-CT), which is the CT reconstructed using the portal images collected during VMAT, can potentially be an effective onsite imaging tool. The goal of this study was to propose an iterative reconstruction algorithm that can further improve the image quality of VMAT-CT and reduce the number of failed reconstructions. An iterative algorithm combining total variation (TV) with block-matching and 3D filtering (BM3D) was proposed, addressing the L1-L2 regularization problem using the split Bregman method. We collected portal images from 67 VMAT cases including 50 phantom and 17 real-patient cases. Both Feldkamp-Davis-Kress (FDK) and TV-BM3D iterative algorithms were used to reconstruct VMAT-CT using the collected images. The preprocessing methods developed by our group previously were also used in this study. A total of 48 out of 50 phantom cases and 15 out of 17 real-patient cases were successfully reconstructed using the iterative algorithm together with image preprocessing. In contrast, 39 phantom cases and 8 patient cases could be reconstructed using the original FDK algorithm, and 44 phantom cases and 11 patient cases could be reconstructed using the FDK algorithm together with preprocessing. Compared with the FDK algorithm, the TV-BM3D iterative algorithm significantly improved the image quality of VMAT-CT at all treatment sites. To the best of our knowledge, this study is the first to develop an iterative VMAT-CT reconstruction algorithm. It can be used to reconstruct CT images locally, and is superior to FDK-based algorithms in terms of the success rate and reconstructed image quality. This strongly supports the use of VMAT-CT as a promising imaging tool for treatment monitoring and adaptive radiotherapy.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13118252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

D²MNet: Difference-Aware Decoupling and Multi-Prompt Learning for Medical Difference Visual Question Answering. D2MNet：医学差异视觉问答的差异感知解耦与多提示学习。

IF 2.7

Journal of Imaging Pub Date : 2026-04-09 DOI: 10.3390/jimaging12040162

Lingge Lai, Weihua Ou, Jianping Gou, Zhonghua Liu

{"title":"D2MNet: Difference-Aware Decoupling and Multi-Prompt Learning for Medical Difference Visual Question Answering.","authors":"Lingge Lai, Weihua Ou, Jianping Gou, Zhonghua Liu","doi":"10.3390/jimaging12040162","DOIUrl":"https://doi.org/10.3390/jimaging12040162","url":null,"abstract":"Difference visual question answering (Diff-VQA) aims to answer questions by identifying and reasoning about differences between medical images. Existing methods often rely on simple feature subtraction or fusion to model image differences, while overlooking the asymmetric descriptive requirements of changed and unchanged cases and providing limited task-specific guidance to pretrained language decoders. To address these limitations, we propose D2MNet (Difference-aware Decoupling and Multi-prompt Network), a framework for medical Diff-VQA that combines change-aware reasoning with prompt-guided answer generation. Specifically, a Change Analysis Module (CAM) predicts whether a change is present and produces a binary change-aware prompt; a Difference-Aware Module (DAM) uses dual attention to capture fine-grained difference features; and a multi-prompt learning mechanism (MLM) injects question-aware, change-aware, and learnable prompts into the language decoder to improve contextual alignment and response generation. Experiments on the MIMIC-DiffVQA benchmark show that D2MNet achieves a CIDEr score of 2.907 ± 0.040, outperforming the strongest baseline, ReAl (2.409), under the same evaluation setting. These results demonstrate the effectiveness of the proposed design on benchmark medical Diff-VQA and suggest its potential for assisting difference-aware medical answer generation.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Robust Rule-Based Framework for Stone Detection and Posterior Acoustic Shadow Localization in Abdominal Ultrasound. 基于鲁棒规则的腹部超声结石检测和后声影定位框架。

IF 2.7

Journal of Imaging Pub Date : 2026-04-09 DOI: 10.3390/jimaging12040163

Kyuseok Kim, Ji-Youn Kim

{"title":"A Robust Rule-Based Framework for Stone Detection and Posterior Acoustic Shadow Localization in Abdominal Ultrasound.","authors":"Kyuseok Kim, Ji-Youn Kim","doi":"10.3390/jimaging12040163","DOIUrl":"https://doi.org/10.3390/jimaging12040163","url":null,"abstract":"Posterior acoustic shadowing is a fundamental physical phenomenon associated with calcified stones in ultrasound image, yet it has not been fully exploited in automated ultrasound analysis. This study aimed to develop an explainable, semi-automatic rule-based framework that explicitly incorporates posterior acoustic shadow characteristics for stone detection and localization in a clinically guided manner. A rule-based framework was designed to generate stone candidates using morphological enhancement and to evaluate them through local contrast analysis, posterior shadow region assessment, and shape-based penalties. A composite score integrating these features was used to rank candidates. The method was evaluated on 52 kidney stone and 66 gallbladder stone ultrasound images, stratified into three diagnostic confidence categories. Performance was assessed using an ablation study and centroid distance error measured in pixels relative to expert-defined references. In the 50-60% confidence group, the accuracy increased from 0.29 to 0.64 for kidney stones and from 0.30 to 0.60 for gallbladder stones when posterior shadow information was included. Centroid distance errors in the ≥80% confidence group were 1.26 ± 0.28 mm for kidney stones and 1.44 ± 0.91 mm for gallbladder stones. The proposed framework enhances diagnostic confidence by leveraging physically grounded posterior acoustic shadow analysis and provides a reproducible augmentation to conventional ultrasound-based stone assessment.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Assessment of Hyperspectral Image Segmentation Algorithms for Fruit Defect Detection Under Different Illumination Conditions. 不同光照条件下水果缺陷高光谱图像分割算法的比较评价。

IF 2.7

Journal of Imaging Pub Date : 2026-04-08 DOI: 10.3390/jimaging12040160

Anastasia Zolotukhina, Anton Sudarev, Georgiy Nesterov, Demid Khokhlov

{"title":"Comparative Assessment of Hyperspectral Image Segmentation Algorithms for Fruit Defect Detection Under Different Illumination Conditions.","authors":"Anastasia Zolotukhina, Anton Sudarev, Georgiy Nesterov, Demid Khokhlov","doi":"10.3390/jimaging12040160","DOIUrl":"https://doi.org/10.3390/jimaging12040160","url":null,"abstract":"This study presents a comparative analysis of hyperspectral image segmentation algorithms for fruit defect detection under different illumination conditions. The research evaluates the performance of four segmentation methods (Spectral Angle Mapper, Random Forest, Support Vector Machine, and Neural Network) using three distinct illumination modes (local, simultaneous and sequential). The experimental setup employed hyperspectral imaging to assess tomato fruit samples, with data acquisition performed across the 450-850 nm spectral range. Quantitative metrics, including accuracy, error rate, precision, recall, F1-score, and Intersection over Union (IoU), were used to evaluate algorithm performance. Key findings indicate that Random Forest demonstrated superior performance across most metrics, particularly under simultaneous illumination conditions. The highest accuracy was achieved by Random Forest under sequential illumination (0.9971), while the best combination of segmentation metrics was obtained under simultaneous illumination, with an F1-score of 0.8996 and an IoU of 0.8176. The Neural Network showed competitive results. The Spectral Angle Mapper proved sensitive to illumination variations but excelled in specific scenarios requiring minimal memory usage. By demonstrating that acquisition protocol optimization can substantially improve segmentation performance, our results support the development of accurate, non-contact, high-throughput inspection systems and contribute to reducing postharvest losses and improving supply chain quality control.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13118074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RegionGraph: Region-Aware Graph-Based Building Reconstruction from Satellite Imagery. RegionGraph：基于区域感知图的卫星图像建筑重建。

IF 2.7

Journal of Imaging Pub Date : 2026-04-08 DOI: 10.3390/jimaging12040161

Lei Li, Chenrong Fang, Wei Li, Kan Chen, Baolong Li, Qian Sun

{"title":"RegionGraph: Region-Aware Graph-Based Building Reconstruction from Satellite Imagery.","authors":"Lei Li, Chenrong Fang, Wei Li, Kan Chen, Baolong Li, Qian Sun","doi":"10.3390/jimaging12040161","DOIUrl":"https://doi.org/10.3390/jimaging12040161","url":null,"abstract":"Structural reconstruction helps infer the spatial relationships and object layouts in a scene, which is an essential computer vision task for understanding visual content. However, it remains challenging due to the high complexity of scene structural topologies in real-world environments. To address this challenge, this paper proposes RegionGraph, a novel method for structural reconstruction of buildings from a satellite image. It utilizes a layout region graph construction and graph contraction approach, introducing a primitive (layout region) estimation network named ConPNet for detecting and estimating different structural primitives. By combining structural extraction and rendering synthesis processes, RegionGraph constructs a graph structure with layout regions as nodes and adjacency relationships as edges, and transforms the graph optimization process into a node-merging-based graph contraction problem to obtain the final structural representation. The experiments demonstrated that RegionGraph achieves a 4% improvement in average F1 scores across three types of primitives and exhibits higher regional completeness and structural coherency in the reconstructed structure.","PeriodicalId":37035,"journal":{"name":"Journal of Imaging","volume":"12 4","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13117877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0