KEYWORDS: Lung, Computed tomography, 3D modeling, 3D image processing, Feature extraction, Education and training, Matrices, Data modeling, Convolution, Performance modeling
As the population ages, early diagnosis and treatment of lung diseases become increasingly important. Accurate assessment of aging-related changes in lung CT images is crucial for the prevention and treatment of related diseases. Traditional methods for lung aging assessment from CT images are time-consuming, subjective, and heavily reliant on the clinical experience of doctors. To address these issues, this paper proposes a lung aging assessment method with 3D-CA Net. The feature extraction part of the proposed network consists of four main 3D Convolutional and Composite Multidimensional Attention Modules. By introducing the Composite Multidimensional Attention Module, the advantages of spatial attention and self-attention are both utilized. Additionally, an improved E-cross-entropy loss function is employed to reduce overfitting and enhance generalization. Experimental results demonstrate that the 3D-CA Net significantly outperforms existing methods in terms of accuracy, macro-averaged precision, macro-averaged recall and macro-averaged F1 score. This work provides a comprehensive solution for lung CT image aging assessment and offers insights for future advancements in medical imaging analysis.
Gastric cancer is a serious health threat and pathological images are an important criterion in its diagnosis. These images can help doctors accurately identify cancerous regions and provide important evidence for clinical decision-making. Thanks to the remarkable achievements of deep learning technology in the field of image processing, an increasing number of superior image segmentation models have emerged. The Swin-Unet model has achieved great success in the field of image segmentation. However, when applied to the image segmentation of gastric cancer pathological section data, the segmentation boundary appears jagged. We have put forth two potential solutions. Initially, we devised an attention connection module to supplant the skip connections within the model, thereby enhancing the model’s predictive precision. Subsequently, we engineered a prognostic processing unit that inputs the model’s predictive outcomes and employs a Conditional Random Field (CRF) for further predictive computations. The enhanced model increases the DSC by 2% and decreases the HD by 17%. Additionally, the issue of jagged boundaries in prediction results has been better optimized. We conducted comparative and ablation experiments, and the results showed that our improved method increased the accuracy of the model’s predictions and reduced the jaggedness of the results at the boundary.
This paper describes an automatic dense correspondence approach to match two given isometric or nearly isometric 3D shapes which have non-rigid deformations. Our method is to improve the described ability of the assignment matrix as much as possible and solve the resolution composed of assignment matrices by using a combinatorial optimization algorithm. First, we construct two linear assignment matrices by using the SHOT and HKS descriptor, which can promote similar points into correspondence. Then, we construct a quadratic assignment matrix by using the heat distribution matrix, which can align a set of pairwise descriptors between a pair of points. In the final, we create a new objective function consisting of three assignment matrices which can adequately describe the matching relationship between points on two non-rigid deformed shapes, and the final optimal solution is obtained by solving the objective function using the projected descent optimization procedure. We show that high-quality dense correspondences can be established for a wide variety of model pairs which may have different poses, surface details. The effectiveness of this method is proven by geodesic error distance statistics from two commonly used datasets with ground truth, and we find that our algorithm is better than other state-of-the-art methods.
Digital watermarking, as an important information security technology, can play a dual role of evidence tracing and encryption in Mobile Police System. In the process of uploading photographic pictures, the poor connectivity and stability of wireless network will greatly affect the robustness of watermarking algorithm. In this study, we proposed a blind watermarking algorithm based on SIFT feature points meanwhile with the idea of spread spectrum.The algorithm extracts sufficient sub-blocks of the host image using feature points to achieve high invisibility and extends multiple times of the original watermark to achieves high robustness and security. Experiment results show that the invisibility and robustness of watermark by using SIFT feature points are better than several other feature extractors and the algorithm meets the application requirements of Mobile Police System.
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