International Journal of Imaging Systems and Technology最新文献

Brain tumors pose a significant threat to human health, necessitating early detection and accurate diagnosis to enhance treatment outcomes. However, centralized data collection and processing encounter challenges related to privacy breaches and data integration due to the sensitivity and diversity of brain tumor patient data. In response, this paper proposes an innovative federated learning-based approach for brain tumor detection, facilitating multicenter data sharing while safeguarding individual data privacy. Our proposed federated learning architecture features each medical center as a participant, with each retaining local data and engaging in secure communication with a central server. Within this federated migration learning framework, each medical center independently trains a base model on its local data and transmits a fraction of the model's parameters to the central server. The central server leverages these parameters for model aggregation and knowledge sharing, facilitating the exchange and migration of models among participating medical centers. This collaborative approach empowers individual medical centers to share knowledge and experiences, thereby enhancing the performance and accuracy of the brain tumor detection model. To validate our federated learning model, we conduct comprehensive evaluations using an independent test dataset, comparing its performance with traditional centralized learning approaches. The experimental results underscore the superiority of the federated learning-based brain tumor detection approach, achieving heightened detection performance compared with traditional methods while meticulously preserving data privacy. In conclusion, our study presents an innovative solution for effective data collaboration and privacy protection in the realm of brain tumor detection, holding promising clinical applications. The federated learning approach not only advances detection accuracy but also establishes a secure and privacy-preserving foundation for collaborative research in medical imaging.

Classifying fetal ultrasound images into different anatomical categories, such as the abdomen, brain, femur, thorax, and so forth can contribute to the early identification of potential anomalies or dangers during prenatal care. Ignoring major abnormalities that might lead to fetal death or permanent disability. This article proposes a novel hybrid capsule network architecture-based method for identifying fetal ultrasound images. The proposed architecture increases the precision of fetal image categorization by combining the benefits of a capsule network with a convolutional neural network. The proposed hybrid model surpasses conventional convolutional network-based techniques with an overall accuracy of 0.989 when tested on a publicly accessible dataset of prenatal ultrasound images. The results indicate that the proposed hybrid architecture is a promising approach for precisely and consistently classifying fetal ultrasound images, with potential uses in clinical settings.

Accurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U-Net structure. The enhanced U-Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze-and-excitation blocks) to adaptively extract image features after each U-Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U-Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance.

Liver cirrhosis is one of the most common liver diseases in the world, posing a threat to people's daily lives. In advanced stages, cirrhosis can lead to severe symptoms and complications, making early detection and treatment crucial. This study aims to address this critical healthcare challenge by improving the accuracy of liver cirrhosis classification using ultrasound imaging, thereby assisting medical professionals in early diagnosis and intervention. This article proposes a new multiscale feature fusion network model (MSFNet), which uses the feature extraction module to capture multiscale features from ultrasound images. This approach enables the neural network to utilize richer information to accurately classify the stage of cirrhosis. In addition, a new loss function is proposed to solve the class imbalance problem in medical datasets, which makes the model pay more attention to the samples that are difficult to classify and improves the performance of the model. The effectiveness of the proposed MSFNet was evaluated using ultrasound images from 61 subjects. Experimental results demonstrate that our method achieves high classification accuracy, with 98.08% on convex array datasets and 97.60% on linear array datasets. Our proposed method can classify early, middle, and late cirrhosis very accurately. It provides valuable insights for the clinical treatment of liver cirrhosis and may be helpful for the rehabilitation of patients.

Kidney tumours are one of the most common tumours in humans and the main current treatment is surgical removal. The CT images are usually manually segmented by a specialist for pre-operative planning, but this can be influenced by the surgeon's experience and skill and can be time-consuming. Due to the complex lesions and different morphologies of kidney tumours that make segmentation difficult, this article proposes a convolutional neural network-based automatic segmentation method for CT images of kidney tumours to address the most common problems of boundary blurring and false positives in tumour segmentation images. The method is highly accurate and reliable, and is used to assist doctors in surgical planning as well as diagnostic treatment, relieving medical pressure to a certain extent. The EfficientNetV2-UNet segmentation model proposed in this article includes three main parts: feature extractor, reconstruction network and Bayesian decision algorithm. Firstly, for the phenomenon of tumour false positives, the EfficientNetV2 feature extractor, which has high training accuracy and efficiency, is selected as the backbone network, which extracts shallow features such as tumour location, morphology and texture in the CT image by downsampling. Secondly, on the basis of the backbone network, the reconstruction network is designed, which mainly consists of conversion block, deconvolution block, convolution block and output block. Then, the up-sampling architecture is constructed to gradually recover the spatial resolution of the feature map, fully identify the contextual information and form a complete encoding–decoding structure. Multi-scale feature fusion is achieved by superimposing all levels of feature map channels on the left and right sides of the network, preventing the loss of details and performing accurate tumour segmentation. Finally, a Bayesian decision algorithm is designed for the edge blurring phenomenon of segmented tumours and cascaded over the output of the reconstruction network, combining the edge features of the original CT image and the segmented image for probability estimation, which is used to improve the accuracy of the model edge segmentation. Medical images in NII special format were converted to Numpy matrix format using python, and then more than 2000 CT images containing only kidney tumours were selected from the KiTS19 dataset as the dataset for the model, and the dimensions were standardised to 128 × 128, and the experimental results show that the model outperforms many other advanced models with good segmentation performance.

Breast cancer is a widespread health threat for women globally, often difficult to detect early due to its asymptomatic nature. As the disease advances, treatment becomes intricate and costly, ultimately resulting in elevated fatality rates. Currently, despite the widespread use of advanced machine learning (ML) and deep learning (DL) techniques, a comprehensive diagnosis of breast cancer remains elusive. Most of the existing methods primarily utilize either attention-based deep models or models based on handcrafted features to capture and gather local details. However, both of these approaches lack the capability to offer essential local information for precise tumor detection. Additionally, the available breast cancer datasets suffer from class imbalance issue. Hence, this paper presents a novel weighted average ensemble network (WA-ENet) designed for early-stage breast cancer detection that leverages the ability of ensemble technique over single classifier-based models for more robust and accurate prediction. The proposed model employs a weighted average-based ensemble technique, combining predictions from three diverse classifiers. The optimal combination of weights is determined using the hill climbing (HC) algorithm. Moreover, the proposed model enhances overall system performance by integrating deep features and handcrafted features through the use of HOG, thereby providing precise local information. Additionally, the proposed work addresses class imbalance by incorporating borderline synthetic minority over-sampling technique (BSMOTE). It achieves 99.65% accuracy on BUSI and 97.48% on UDIAT datasets.

This study explores the potential of halophytes, plants adapted to saline environments, as a novel source for developing herbal MRI contrast agents. Halophytes naturally accumulate various metals within their tissues. These metal ions, potentially complexed with organic molecules, are released into aqueous solutions prepared from the plants. We investigated the ability of these compounds to generate contrast enhancement in MRI using a sequential approach. First, aqueous extracts were prepared from seven selected halophytes, and their capacity to induce contrast in MR images was evaluated. Based on these initial findings, sample halophytes were chosen for further investigations. Second, chemical analysis revealed aluminum as the primary potent metal which enhances the contrast. Third, the halophyte extract was fractionated based on polarity, and the most polar fraction exhibited the strongest contrast-generating effect. Finally, the relaxivity of this fraction, a key parameter for MRI contrast agents, was measured. We propose that aluminum, likely complexed with a polar molecule within the plant extract, is responsible for the observed contrast enhancement in MRI.

Breast masses are often one of the primary signs of breast cancer, and precise segmentation of these masses is essential for accurate diagnosis and treatment planning. Diagnosis may be complex depending on the size and visibility of the mass. When the mass is not visible clearly, precise segmentation becomes very difficult and in that case enhancement is essential. Inadequate compression, patient movement, or paddle/breast movement during the exposure process might cause hazy mammogram images. Without enhancement, accurate segmentation and detection cannot be done. As there exists uncertainties in different regions, reducing uncertainty is still a main problem and so fuzzy methods may deal these uncertainties in a better way. Though there are many fuzzy and advanced fuzzy methods, we consider Pythagorean fuzzy set as one of the fuzzy sets that may be powerful to deal with uncertainty. This research proposes a new Pythagorean fuzzy methodology for mammography image enhancement. The image is first transformed into a fuzzy image, and the nonmembership function is then calculated using a newly created Pythagorean fuzzy generator. Membership function of Pythagorean fuzzy image is computed from nonmembership function. The plot between the membership value and the hesitation degree is used to calculate a constant term in the membership function. Next, an enhanced image is obtained by applying fuzzy intensification operator to the Pythagorean fuzzy image. The proposed method is compared qualitatively and quantitatively with those of non-fuzzy, intuitionistic fuzzy, Type 2 fuzzy, and Pythagorean fuzzy methods, it is found that the suggested method outperforms the other methods. To show the usefulness of the proposed enhanced method, segmentation is carried out on the enhanced images.

In the development of medical devices and surgical robot systems, animal models are often used for evaluation, necessitating accurate organ segmentation. Deep learning-based image segmentation provides a solution for automatic and precise organ segmentation. However, a significant challenge in this approach arises from the limited availability of training data for animal models. In contrast, human medical image datasets are readily available. To address this imbalance, this study proposes a fine-tuning approach that combines a limited set of animal model images with a comprehensive human image dataset. Various postprocessing algorithms were applied to ensure that the segmentation results met the positioning requirements for the evaluation of a medical robot under development. As one of the target applications, magnetic resonance images were used to determine the position of the dog's prostate, which was then used to determine the target location of the robot under development. The MSD TASK5 dataset was used as the human dataset for pretraining, which involved a modified U-Net network. Ninety-nine pretrained backbone networks were tested as encoders for U-Net. The cross-training validation was performed using the selected network backbone. The highest accuracy, with an IoU score of 0.949, was achieved using the independent validation set from the MSD TASK5 human dataset. Subsequently, fine-tuning was performed using a small set of dog prostate images, resulting in the highest accuracy of an IoU score of 0.961 across different cross-validation groups. The processed results demonstrate the feasibility of the proposed approach for accurate prostate segmentation.