Inference of tumor and edema areas from brain magnetic resonance imaging (MRI) data remains challenging owing to the complex structure of brain tumors, blurred boundaries, and external factors such as noise. To alleviate noise sensitivity and improve the stability of segmentation, an effective hybrid clustering algorithm combined with morphological operations is proposed for segmenting brain tumors in this paper. The main contributions of the paper are as follows: firstly, adaptive Wiener filtering is utilized for denoising, and morphological operations are used for removing nonbrain tissue, effectively reducing the method’s sensitivity to noise. Secondly, K-means++ clustering is combined with the Gaussian kernel-based fuzzy C-means algorithm to segment images. This clustering not only improves the algorithm’s stability, but also reduces the sensitivity of clustering parameters. Finally, the extracted tumor images are postprocessed using morphological operations and median filtering to obtain accurate representations of brain tumors. In addition, the proposed algorithm was compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity, and recall.
Sensor node localization is the basis for the entire wireless sensor
networks. Because of restricted energy of the sensor nodes, the location
error, costs of communication and computation should be considered in
localization algorithms. DV-Hop localization algorithm is a typical
positioning algorithm that has nothing to do with distance. In the isotropic
dense network, DV-Hop can achieve position more precisely, but in the random
distribution network, the node location error is great. This paper summed up
the main causes of error based on the analysis on the process of the DV-Hop
algorithm, aimed at the impact to the location error which is brought by the
anchor nodes of different position and different quantity, a novel
localization algorithm called NDVHop_Bon (New DV-Hop based on optimal
nodes) was put forward based on optimal nodes, and it was simulated on
Matlab. The results show that the new proposed location algorithm has a
higher accuracy on localization with a smaller communication radius in the
circumstances, and it has a wider range of applications.
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