The traditional [ ]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.

The segmentation of medical images, particularly for brain tumors, is essential for clinical diagnosis and treatment planning. In this study, we proposed MMAformer, a Multiscale Modality-Aware Transformer model, which is designed for segmenting brain tumors by utilizing multimodality magnetic resonance imaging (MRI). Complementary information between different sequences helps the model delineate tumor boundaries and distinguish different tumor tissues. To enable the model to acquire the complementary information between related sequences, MMAformer employs a multistage encoder, which uses a cross-modal downsampling (CMD) block for learning and integrating the complementary information between sequences at different scales. In order to effectively fuse the various information extracted by the encoder, the Multimodal Gated Aggregation (MGA) block combines the dual attention mechanism and multi-gated clustering to effectively fuse the spatial, channel, and modal features of different MRI sequences. In the comparison experiments on the BraTS2020 and BraTS2021 datasets, the average Dice score of MMAformer reached 86.3% and 91.53%, respectively, indicating that MMAformer surpasses the current state-of-the-art approaches. MMAformer’s innovative architecture, which effectively captures and integrates multimodal information at various scales, offers a promising solution for tackling complex medical image segmentation challenges.

An important prerequisite for brain disease diagnosis is to segment brain tissues of Magnetic Resonance Imaging (MRI) into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). To improve performance, we propose a Multi-modality Reconstruction Attention and Difference Enhancement Network (MRADE-Net). We first stack three inputs of multiple MRI modalities along axial, sagittal, and coronal axes to form three enlarged volumes. Then we adopt global average pooling along each axis, fully connected layers, and activation functions to produce three orthogonal 2D coefficient maps, which are used to reconstruct a 3D attention map for weighting the three inputs. These weighted inputs are added together to generate a feature map of Multi-modality Reconstruction Attention (MRA). Similarly, we present Single-modality Reconstruction Attention (SRA) for improving feature representation abilities in middle stages. In addition, the difference of encoding features between adjacent layers is used to compensate the loss of spatial information caused by down-sampling. Experimental results show that the proposed approach is more effective than the existing state-of-the-art segmentation methods and its performance is verified on several datasets.

Demagnetization effect plays an important role in the magnetic core design of the orthogonal fluxgate sensor. In this paper, a meander-core orthogonal fluxgate sensor based on amorphous ribbon is described. The demagnetization model of meander-core structures is established, and the average demagnetization factor can be evaluated by finite element modeling. Simulation and experimental analyses were performed to study the effects of demagnetization on the sensitivity and linear range of orthogonal fluxgate sensors in the fundamental mode by varying the number of strips, the line width, and the spacing of the meander-cores. The results were compared and revealed a very close match. The results show that the demagnetization factor increases with an increase in the number of strips and the line width, which leads to an increase in the linear range of the sensors. The sensitivity can be improved by increasing the number of strips appropriately, however, it is reduced when the line width increases. Smaller spacing results in a larger demagnetization factor due to the magnetic interactions between adjacent strips, which reduces the sensitivity of the sensor. The results obtained here from simulations and experiments are useful for designing magnetic sensors with similar structures.