International Journal of Imaging Systems and Technology最新文献

{"title":"A Novel Multimodal Medical Image Fusion Method Based on Detail Enhancement and Dual-Branch Feature Fusion","authors":"Kun Zhang,&nbsp;Hui Yuan,&nbsp;Zhongwei Zhang,&nbsp;PengPeng Sun","doi":"10.1002/ima.70181","DOIUrl":"https://doi.org/10.1002/ima.70181","url":null,"abstract":"<div>\u0000 \u0000 <p>Multimodal medical image fusion integrates effective information from different modal images and integrates salient and complementary features, which can more comprehensively describe the condition of lesions and make medical diagnosis results more reliable. This paper proposes a multimodal medical image fusion method based on image detail enhancement and dual-branch feature fusion (DEDF). First, the source images are preprocessed by guided filtering to enhance important details and improve the fusion and visualization effects. Then, local extreme maps are used as guides to smooth the source images. Finally, a DEDF mechanism based on guided filtering and bilateral filtering is established to obtain multiscale bright and dark feature maps, as well as base images of different modalities, which are fused to obtain a more comprehensive medical image and improve the accuracy of medical diagnosis results. Extensive experiments, compared qualitatively and quantitatively with various state-of-the-art medical image fusion methods, validate the superior fusion performance and effectiveness of the proposed method.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BATU: A Workflow for Multi-Network Ensemble Learning in Cross-Dataset Generalization of Skin Lesion Analysis","authors":"Ömer Faruk Söylemez","doi":"10.1002/ima.70183","DOIUrl":"https://doi.org/10.1002/ima.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>The development of computer vision systems for dermatological diagnosis is often hindered by dataset heterogeneity, including differences in image quality, labeling strategies, and patient demographics. In this study, we examine how such heterogeneity affects the generalization ability of computer vision models across three public dermatology image datasets. We trained five different deep learning models on each dataset separately and evaluated their performance in both intra-dataset and cross-dataset settings. To further investigate robustness, we conducted multi-source domain generalization experiments by training models on combinations of two datasets and testing on the third unseen dataset. We observed a significant drop in performance during cross-dataset evaluations. To address this, we applied various ensemble learning methods by combining the predictions from the individual models. Our results demonstrate that ensemble approaches consistently outperform individual models, achieving accuracy improvements exceeding 4% in many cases. These findings highlight the potential of ensemble learning to address challenges related to dataset variability in dermatological image analysis.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144853691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognizing of Vocal Fold Disorders From High Speed Video: Use of Spatio-Temporal Deep Neural Networks","authors":"Dhouha Attia,&nbsp;Amel Benazza-Benyahia","doi":"10.1002/ima.70170","DOIUrl":"https://doi.org/10.1002/ima.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>This work aims at designing advanced computer-aided diagnosis systems leveraging deep learning approaches to identify vocal fold (VF) disorders by analyzing high-speed videos of the laryngeal area. The challenges lie in the high dimensionality of video data, the need for precise temporal resolution to capture rapid glottal dynamics, and the inherent variability in VF motion across individuals. Additionally, distinguishing pathological patterns from normal variations remains a complex task due to subtle and overlapping disorder characteristics. The primary objective of this research lies in showcasing the improvement in classification performance achieved when both temporal and spatial information is incorporated into the analysis. Temporal information, in particular, plays a crucial role when combined with spatial data, as it provides a more comprehensive understanding of dynamic vocal fold behaviors. To address this issue, we highlight the importance of creating specifically designed inputs for the deep neural network that capture the temporal dynamics of the glottal cycle. This ensures that the temporal variability inherent in the glottal cycle is appropriately represented in the input data. A key innovative aspect of this work involves the exploration and evaluation of various spatio-temporal deep learning architectures. These models are systematically compared to traditional architectures that rely solely on spatial information. The comparative analysis aims to determine to what extent incorporating temporal information can improve diagnostic accuracy. Among the tested models, the transformer-based architectures ViViT and TimeSformer achieve the best objective performance in terms of F1-score (around 0.93), ViViT having the least weight. In summary, this paper underscores the importance of utilizing spatio-temporal information from the region of interest for more effective identification of VF disorders. Using both 3D deep learning models and transformer-based architectures, our approach offers a robust solution to diagnose vocal fold pathologies, paving the way for future advancements in computer-aided medical diagnostics.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144767724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal MRI-Based Glioma Segmentation and MGMT Promoter Methylation Status Prediction Using Multitask Learning Architecture","authors":"Jingyu Zhu,&nbsp;Yuehui Liao,&nbsp;Yu Chen,&nbsp;Feng Gao,&nbsp;Ruipeng Li,&nbsp;Guang Yang,&nbsp;Xiaobo Lai,&nbsp;Jing Chen","doi":"10.1002/ima.70173","DOIUrl":"https://doi.org/10.1002/ima.70173","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate identification of brain tumor regions (glioma segmentation) and prediction of O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation are essential for guiding therapy in glioma patients. Typically, these tasks are conducted separately, limiting performance by neglecting the relationship between tumor localization and methylation status. To address this gap, we propose TAUM-Net, a multitask learning model that simultaneously performs glioma segmentation and MGMT promoter methylation prediction from MRI scans. TAUM-Net merges convolutional neural networks (CNNs), which capture local tumor details, with a Transformer architecture modeling global structural features. It uses two branches: one refines tumor boundaries, while the other aggregates multi-scale information to predict MGMT promoter methylation, both of which share a unified representation that optimizes the two tasks in tandem. Evaluations on the BraTS2021 and TCGA-GBM datasets demonstrate TAUM-Net's effectiveness, attaining a Dice score of 0.9210 for glioma segmentation and 63.48% accuracy for MGMT promoter methylation prediction. This performance underscores the value of multitask learning in leveraging shared features, maintaining high segmentation quality, and providing moderate predictive accuracy for methylation status. Although TAUM-Net's current accuracy does not yet replace standard clinical tests, it highlights the potential of integrated approaches for guiding diagnosis and treatment planning. Our code is freely available at https://github.com/smallboy-code/TAUM-Net.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Plus Disease in Retinopathy of Prematurity Using Deep Learning Models: Evaluating Vision Transformers and ResNet Architectures","authors":"Ibrahim Kocak,&nbsp;Sadık Etka Bayramoglu,&nbsp;Nihat Sayin,&nbsp;Lukman Thalib","doi":"10.1002/ima.70174","DOIUrl":"https://doi.org/10.1002/ima.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>To evaluate the performance of Vision Transformer (ViT) and ResNet-50 in detecting Plus Disease (PD) on fundus color images and vascular segmented mask images of Retinopathy of Prematurity (ROP) patients. A dataset consisting of 1205 fundus color images of ROP patients was extracted from the registry of a leading Research Hospital in Istanbul. Using these fundus images, a second dataset of vascular segmented mask images was created with a U-net segmentation model. The performance of ViT and ResNet models in detecting Plus Disease was evaluated on both sets of images. External validation of the model performances was carried out using a public domain dataset. For fundus color images, ViT models performed better than ResNet in terms of accuracy (96.9% vs. 91.5%), precision (97.1% vs. 85.5%), and F1 score (96.9% vs. 92.2%). However, ResNet had a better recall rate (100% vs. 96.9%). For segmented images, all performance measures were better with ResNet than ViT: accuracy (91.5% vs. 82.7%), precision (85.5% vs. 82.9%), recall (100% vs. 92.3%), F1 scores (92.2% vs. 82.6%), and AUC (99.8% vs. 88.6%). The strong performance of the ViT on fundus color images highlights its potential as a promising model for PD detection. However, its higher computational cost suggests that further optimization will be needed in future research. ResNet-50, with its solid overall performance and perfect recall rate—ensuring no false negatives—appears to be an optimal choice for PD detection. Additionally, vascular segmentation did not provide any enhancement to the model performances.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144758587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ELK-BiONet: Efficient Large-Kernel Convolution Enhanced Recurrent Bidirectional Connection Encoding and Decoding Structure for Skin Lesions Segmentation","authors":"Jingjing Ma,&nbsp;Zhanxu Liu,&nbsp;Zhiqiang Guo,&nbsp;Ping Wang","doi":"10.1002/ima.70172","DOIUrl":"https://doi.org/10.1002/ima.70172","url":null,"abstract":"<div>\u0000 \u0000 <p>The size and shape of skin lesions often exhibit significant variability, and enabling deep learning networks to adapt to this variability is crucial for improving the segmentation performance of such lesions. The encoder-decoder architecture has become one of the most commonly used structures for semantic segmentation in deep learning models. However, when the convolution-based UNet network is applied to skin lesion segmentation, several issues remain. (1) Traditional small-kernel convolutions have a limited receptive field, which makes it difficult to adapt to the varying sizes and shapes of skin lesions. (2) The conventional U-Net architecture experiences a substantial increase in parameter count as the network depth grows. (3) Although the U-Net decoder utilizes feature information from the encoder, the features extracted by the decoder are not fully leveraged. To address the above challenges in U-Net for skin lesion segmentation tasks, we propose an efficient large-kernel convolution enhanced recurrent bidirectional connection encoding and decoding structure for skin lesions segmentation (ELK-BiONet). The main innovations of this method are as follows: (1) We propose a large-kernel convolution method that balances large and small receptive fields while maintaining a relatively low parameter count. (2) The network extracts feature information in a recurrent manner, allowing the construction of deeper network architectures while keeping the overall parameter count nearly constant. (3) By employing bidirectional connections, the features extracted by the decoder are fully utilized in the encoder, thereby enhancing the segmentation performance of the network. We evaluated our method on skin lesion segmentation tasks, and the results demonstrate that our ELK-BiONet significantly outperforms other segmentation methods.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 5","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Metaheuristic-Based Conductivity Distribution Optimization Method for Accurate Imaging in Electrical Impedance Tomography","authors":"Yanyan Shi,&nbsp;Yan Cui,&nbsp;Meng Wang,&nbsp;Zhenkun Liu,&nbsp;Feng Fu","doi":"10.1002/ima.70175","DOIUrl":"https://doi.org/10.1002/ima.70175","url":null,"abstract":"<div>\u0000 \u0000 <p>In the medical application of electrical impedance tomography (EIT), image reconstruction of conductivity distribution is essential for diagnosis of physiological or pathological changes. In this study, a metaheuristic-based conductivity distribution optimization method is proposed for accurate reconstruction. To test the performance, simulation work is conducted and different models are reconstructed. Images reconstructed by the Newton–Raphson method, Tikhonov method, and genetic algorithm have been adopted for comparison. The effect of noise on the proposed method is also investigated. In addition to simulation, a phantom experiment is designed to further testify to the effectiveness of the proposed method. The results show that the proposed method outperforms other comparative methods in conductivity distribution imaging. The proposed method gives a more precise reconstruction of the inclusion, with a notably clearer background. Meanwhile, the proposed method is more robust to noise. It offers an effective alternative for conductivity distribution reconstruction in the application of EIT.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Efficient Brain Tumor Segmentation via a Transformer-Driven 3D U-Net","authors":"Runlin Chen,&nbsp;Huixuan Luo,&nbsp;Yanming Ren,&nbsp;Wenjie Liu,&nbsp;Wenyao Cui","doi":"10.1002/ima.70158","DOIUrl":"https://doi.org/10.1002/ima.70158","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate brain tumor segmentation is critical for clinical diagnosis and treatment. The rapid development of deep neural networks (DNNs) in computer vision offers an automated solution for segmentation tasks. However, convolutional neural networks (CNNs) cannot model long-range dependencies, hindering their perception of global information on tumors. Moreover, vision Transformers (ViTs) require extensive annotated data for optimal segmentation performance, leading to high computational costs and overfitting on small datasets. To address these challenges, we propose TDU-Net, an efficient and accurate brain tumor segmentation scheme using Transformer-driven 3D U-Net. In TDU-Net, improved inverted residual bottlenecks with large kernels are employed in both downsampling and upsampling blocks, optimizing memory efficiency while maintaining global semantic richness in 3D multimodal tumor data. Inspired by ViT, fewer activation functions and normalization layers are used in downsampling and upsampling blocks. GELU activation, group normalization, and larger convolution kernels are employed to improve the global perception and segmentation capability on small datasets. Additionally, orthogonal regularization is introduced during training to mitigate overfitting and enhance generalizability. Experimental results demonstrate that TDU-Net achieves superior brain tumor segmentation accuracy with fewer model parameters, thereby improving generalizability and reducing performance degradation due to overfitting.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modified Transformer-Based Pixel Segmentation for Breast Tumor Detection","authors":"Kamakshi Rautela,&nbsp;Dinesh Kumar,&nbsp;Vijay Kumar","doi":"10.1002/ima.70166","DOIUrl":"https://doi.org/10.1002/ima.70166","url":null,"abstract":"<div>\u0000 \u0000 <p>This study introduces a novel hybrid deep learning model that combines residual convolutional networks and a multilayer perceptron (MLP)-based transformer for precise breast lesion segmentation and classification using mammogram images. Initially, mammograms undergo preprocessing involving thresholding and Gabor-based pixel segmentation to extract informative patches. The proposed model leverages deep features extracted via convolutional neural networks, which are subsequently processed through self-attention and cross-attention mechanisms in a modified transformer architecture to capture both local and global dependencies for classification. The approach is rigorously evaluated on the publicly available INbreast dataset, achieving classification accuracies of 98.17% for a three-class (normal, benign, malignant) scenario and 96.74% for a more detailed five-class classification. The model demonstrates strong capabilities in differentiating subtle variations between malignant and benign tissues. These promising results suggest significant potential for practical clinical implementation, assisting radiologists by providing highly accurate diagnostic insights. Notably, this approach contributes substantially to automated breast cancer diagnostics, highlighting the efficacy of integrating convolutional neural network features with transformer architectures for improved segmentation and classification outcomes.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144681247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CMFR-Net: Cross Multi-Scale Features Refinement Network for Medical Image Segmentation","authors":"Huimin Guo,&nbsp;Yonglai Zhang,&nbsp;Hualing Li,&nbsp;Gaizhen Liu,&nbsp;Jiaxin Huo","doi":"10.1002/ima.70167","DOIUrl":"https://doi.org/10.1002/ima.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>The automation of medical image segmentation can assist doctors in quickly and accurately extracting lesion regions, reducing their workload in clinical analysis, improving diagnostic efficiency, and aiding in the early diagnosis and analysis of diseases. However, medical images are susceptible to noise, and variations in the position, size, and shape of organs and tissue structures across different patients pose significant challenges in achieving accurate segmentation. In this paper, we propose the Cross Multi-scale Features Refinement Network (CMFR-Net), which introduces the cross features enhancement (CFE) module, the boundary refinement (BR) module, and the global context features guidance (GCFG) module to extract multi-scale spatial information and boundary details of the target region, capture long-range feature dependencies, and improve segmentation performance. The CFE module captures local feature information from target regions at different scales, the BR module alleviates boundary blurring issues during segmentation, and the GCFG module strengthens the model's ability to capture global features and spatial positional information. Experiments conducted on three public datasets and one private dataset demonstrate the effectiveness of the proposed CMFR-Net. The Dice coefficients of CMFR-Net on the four datasets reached 87.35%, 87.65%, 97.52%, and 88.38%, respectively.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}