No abstract

In medical image segmentation, the neural network structure of the U‐Net family has demonstrated sufficient advantages. However, MRI images have different scan parameters and different scan times, resulting in different feature representation of the images. Furthermore, there is a great class imbalance between bone and cartilage tissues in MRI knee images. To address these issues, a region‐based two‐stage MRI knee bone tissue segmentation network is proposed in this paper. The segmentation network makes full use of the location characteristics of the three types of bone tissue in the knee joint and uses a two‐stage network architecture with a modified U2‐Net backbone network to segment MRI knee bone tissue. The neural network structure is divided into two phases, the first phase with a simple coded decoding structure for saliency detection to obtain the positional regional relationships of different bone tissues, and the second phase with a segmentation network consisting of 2 modified U2‐Net, one for segmenting the patella and associated cartilage and the other for segmenting the femur, tibia and associated cartilage. The algorithm was tested with a variety of MRI knee data to verify the effectiveness of the algorithm.

No abstract

Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.

No abstract