International Journal of Imaging Systems and Technology最新文献

Medical image segmentation faces the challenge of balancing multiscale anatomical structure modeling and computational efficiency. To address this issue, this paper proposes a “Frequency-Attentive Multi-Hierarchical Network for Medical Image Segmentation” (FreqAtt-MultHier-Net), aiming to achieve synergistic optimization of accuracy, efficiency, and robustness. The core innovations of this paper include: A dual-frequency block (DFB), which decouples high-frequency (detail) and low-frequency (semantic) features through a learnable channel splitting mechanism, and enhances multiscale representations through cross-frequency interaction and dynamic calibration. A multiscale dual-attention fusion block (MSDAFB), which couples channel-spatial dual attention with multi-kernel convolutions to suppress background noise and strengthen local–global contextual fusion. A lightweight ConvMixer module that replaces Transformers with sublinear computational complexity to achieve efficient long-range dependency modeling. In tasks involving cell contour, cell nucleus, lung cancer, skin cancer, liver tumor segmentation and retinal vessel segmentation Task, our model achieves dice similarity coefficients (DSCs) of 95.64%, 92.74%, 83.63%, 85.96%, 85.86% and 84.26%, respectively, while reducing parameter count (25.48 M) and computational cost (5.84 G FLOPs) by 75.9%–84.9% compared to Transformer-based architectures. Ablation experiments validate the independent contributions of each module, with frequency-domain decoupling improving high-frequency detail retention by 18.8% and lightweight design reducing FLOPs by 78.3%. FreqAtt-MultHier-Net provides a high-precision, low-redundancy general solution for medical image segmentation, with potential for low-power clinical deployment. The code is available at the following URL: https://github.com/wu501-CPU/FreqAtt-MultHier-UNet.

Gliomas are the most common type of primary brain tumor, characterized by their diffuse invasiveness and origin within the central nervous system. Manual identification and segmentation of tumor regions in MRI is a time-consuming and subjective process, and may negatively impact diagnostic accuracy because the heterogeneity and infiltrative pattern of glioma are complex. To address these problems, we propose an automated glioma segmentation approach named IADSG (Intelligent Assistant Diagnosis System for Glioma), based on our novel VMDUnet architecture. Our method incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing step to enhance image contrast and quality. Moreover, we use data augmentation techniques to improve the generalization and adaptability to complex clinical images of the model. Crucially, the integration of a Mamba module and a dual cross-attention mechanism enables the model to effectively balance segmentation accuracy with computational efficiency. Experimental results show that our approach achieves a segmentation accuracy of 0.7769 DSC on the internal glioma dataset and 0.9117 DSC on the public BraTS dataset, outperforming existing segmentation methods on both benchmarks. This approach reduces the time and effort involved in manual segmentation, reduces the probabilities of misdiagnosis, and provides robust support for the diagnosis and treatment to be accurately conducted. Our code is available at https://github.com/CarioAo/VMDUnet.

Multiple sclerosis (MS) is a chronic inflammatory disease-causing neurological disability, particularly in young adults. Magnetic resonance imaging (MRI) is the most effective tool for detecting MS plaques, but contrast-enhanced imaging involves potential risks, including toxicity and increased imaging time. Previous methods for differentiating plaque types, such as texture analysis and manual feature extraction, face challenges such as limited datasets and poor generalizability. This study aims to develop and compare deep learning-based methods, specifically convolutional neural networks (CNNs), to classify MS lesion types using non-contrast MRI, aiming to improve clinical applicability and reduce reliance on contrast agents. This study involved 106 multiple sclerosis (MS) patients from two MRI centers. A total of 3410 lesions were analyzed, including 1408 active and 2002 inactive lesions. MRI images, including T1-weighted imaging with gadolinium contrast (T1 + Gd(, T1, Fluid-Attenuated Inversion Recovery (FLAIR), and T2 sequences, were acquired. The segmented lesions were converted into 2D slices and resampled to 128 × 128 pixels for deep learning input. Data augmentation and normalization were applied to improve model generalizability. A custom CNN model was developed and compared with four pre-trained models (ResNet50, VGG16, DenseNet121, and EfficientNetB0) using fivefold cross-validation to evaluate model performance. Performance metrics including accuracy, sensitivity, specificity, and AUC were used. The custom CNN achieved 90.15% accuracy and 94.67% AUC in FLAIR, outperforming pre-trained models. DenseNet121 showed competitive results with 88.23% accuracy and 92.86% AUC in FLAIR. Non-contrast sequences (T1, T2, and FLAIR) combined with deep learning provided promising results, reducing reliance on contrast agents. The custom CNN model excelled in classifying MS lesions across multiple MRI sequences, offering improved diagnostic accuracy and patient safety. Custom models for specialized datasets can enhance clinical outcomes, demonstrating the potential of deep learning in MS diagnosis. These findings suggest that deep learning models can be replaced with contrast agents in routine practice. Future research may explore combining CNNs with clinical features to enhance performance and interpretability.

Accurate segmentation of cardiac structures is essential for clinical diagnosis and treatment of cardiovascular diseases. Existing Transformer-based cardiac segmentation methods mostly rely on single-scale token-wise attention mechanisms that emphasize global feature modeling, but they lack sufficient sensitivity to local spatial structures, such as myocardial boundaries in cardiac 3D images, resulting in ineffective multiscale feature capturing and a loss of local spatial details, thereby negatively impacting the accuracy of cardiac anatomical segmentation. To address the above issues, this paper proposes a cardiac 3D image segmentation network named MSAF, which integrates Multiscale Collaborative Attention (MSCA) and Multiscale Feature Fusion (MSFF) modules to enhance the multiscale feature perception capability at both microscopic and macroscopic levels, thereby improving segmentation accuracy for complex cardiac structures. Within the MSCA module, a Collaborative Attention (CoA) module combined with hierarchical residual-like connections is designed, enabling the model to effectively capture interactive information across spatial and channel dimensions at various receptive fields and facilitating finer-grained feature extraction. In the MSFF module, a gradient-based feature importance weighting mechanism dynamically adjusts feature contributions from different hierarchical levels, effectively fusing high-level abstract semantic information with low-level spatial details, thereby enhancing cross-scale feature representation and optimizing both global completeness and local boundary precision in segmentation results. Experimental validation of MSAF was conducted on four publicly available medical image segmentation datasets, including ACDC, FlARE21, and MM-WHS (MRI and CT modalities), yielding average Dice values of 93.27%, 88.16%, 92.23%, and 91.22%, respectively. These experimental results demonstrate the effectiveness of MSAF in segmenting detailed cardiac structures.

Melanoma, the deadliest form of skin cancer, requires accurate and timely detection to improve survival rates and treatment outcomes. Deep learning has shown significant potential in automating melanoma detection; however, existing methods face challenges such as irrelevant background information in dermoscopic images and class imbalance in melanoma datasets, which hinder diagnostic performance. To address these challenges, this paper introduces two complementary contributions: Pixel Intensity-Based Masking (PIBM) and Intensity-Weighted Binary Cross-Entropy (IW-BCE). PIBM is a novel preprocessing technique that dynamically identifies and masks low-priority regions in dermoscopic images based on pixel intensity values. By preserving high-intensity lesion regions and suppressing irrelevant background artifacts, PIBM reduces computational complexity and enhances the model's focus on diagnostically critical features, all without requiring ground truth annotations or pixel-level labeling. Additionally, IW-BCE, a custom loss function, is designed to handle class imbalance by dynamically adjusting the contribution of each class during training. By assigning higher weights to the minority class (malignant lesions), IW-BCE enhances the model's sensitivity, reduces false negatives, and improves recall, an essential metric in medical diagnostics. The proposed framework integrates PIBM and IW-BCE into a deep-learning pipeline for melanoma detection. Evaluations on benchmark datasets demonstrate that the combined approach achieves superior performance compared to traditional methods in terms of accuracy, sensitivity, and computational efficiency. Specifically, the proposed method achieves a higher recall and F1-score, highlighting its ability to address the critical limitations of existing systems. This work offers a robust and clinically relevant solution for real-time melanoma detection, paving the way for improved early diagnosis and patient outcomes.

Glaucoma is a leading cause of irreversible blindness, and early diagnosis is critical. While retinal fundus images are commonly used for screening, AI-based diagnostic models face challenges such as data scarcity, class imbalance, and limited image diversity. To address this, we introduce GlaucoDiff, a diffusion-based image synthesis framework designed to generate clinically meaningful glaucoma fundus images. It employs a two-stage training strategy and integrates a multimodal large language model as an automated quality filter to ensure clinical relevance. Experiments on the JustRAIGS dataset show that GlaucoDiff outperforms commercial generators such as DALL-E 3 and Keling, achieving better image quality and diversity (FID: 109.8; SWD: 222.2). When synthetic images were used to augment the training set of a vision transformer classifier, sensitivity improved consistently from 0.8182 with only real data to 0.8615 with 10% synthetic images, and further to 0.8788 with 50%. However, as the proportion of synthetic data increased, other important metrics such as specificity, accuracy, and AUC began to decline compared to the results with 10% synthetic data. This finding suggests that although more synthetic images can enhance the model's ability to detect positive cases, too much synthetic data may reduce overall classification performance. These results demonstrate the practical value of GlaucoDiff in alleviating data imbalance and improving diagnostic accuracy for AI-assisted glaucoma screening.

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.

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.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and structural brain deterioration, with its global prevalence projected to exceed 125 million by 2050. Early and accurate diagnosis—particularly the differentiation of mild cognitive impairment (MCI) from normal aging—is critical for effective intervention; yet it remains challenging due to subtle anatomical changes and high-dimensional imaging data. This study presents a telehealth-compatible computer-aided diagnosis (CAD) framework for multi-class AD classification using structural MRI (sMRI) images from the publicly available ADNI dataset. The framework integrates transfer learning with DenseNet121 (pre-trained on RadImageNet) for deep feature extraction and employs a hybrid bio-inspired particle swarm optimization–genetic algorithm (PSO-GA) for feature selection and dimensionality reduction. This optimized pipeline reduces the original high-dimensional feature space to 16 key features, improving classification accuracy from 88.48% to 99.78% using AdaBoost. The proposed PSO-GA-DenseNet framework delivers a lightweight, scalable solution suitable for remote diagnostic settings. Compared to existing state-of-the-art models, it offers enhanced computational efficiency and robust cross-site adaptability. Future research will focus on improving generalizability across imaging modalities and incorporating longitudinal data to enable real-time, cross-modal, and large-scale deployment in clinical and telehealth environments.

Microwave imaging is a promising technique for non-invasive diagnostics in areas such as medical imaging, remote sensing, and subsurface exploration. Its performance, however, strongly depends on accurate knowledge of background medium properties, particularly electrical conductivity. In practice, these parameters are often uncertain or mismatched, leading to signal degradation and inaccurate reconstructions. This study investigates the impact of conductivity mismatch on the bifocusing method (BFM), a qualitative microwave imaging algorithm, and proposes an improved version incorporating attenuation compensation. Simulations show that conventional BFM fails to identify objects when the assumed conductivity is significantly higher than the actual value. To resolve this, we modify the Green's function by introducing a compensation term based on the attenuation constant, restoring the incident field amplitude. The improved method enables successful object recovery even under severe mismatch. Quantitative evaluation using the Jaccard similarity index confirms improved localization accuracy. This approach enhances the robustness of microwave imaging and shows promise for medical diagnostics in highly attenuating biological tissues.