Multi-view fuzzy clustering analysis is often used for medical image segmentation such as brain MR image segmentation. However, in traditional multi-view clustering, it assumes that each view plays the same role to the final partition result, which omits the negative influences caused by noisy or weak views. In this paper, a novel entropy weighting based centralized clustering technique is proposed for multi-view datasets where the Shannon entropy is hired for view weight learning. Moreover, the centralized strategy is employed for collaborate learning. Extensive experiments show that the promising performance of our proposed clustering technique. More importantly, a case study on brain MR image segmentation indicates the application ability of our clustering technique.

This analysis explores the solutions for wormhole in [Formula: see text] gravity, where [Formula: see text], and [Formula: see text] represent the kinetic term, scalar potential, and Ricci scalar, respectively. For this study, we use the spherically symmetric spacetime with the anisotropic source of matter. Further, we use the linear equation of state to complete this current investigation in the background of conformal symmetry motion. By plugging non-zero conformal Killing vectors, we discuss the feasible phantom wormhole configurations. Under the linear equation of state, the stability of wormhole solutions with specific values of parameters is also checked by using the Tolman–Oppenheimer–Volkoff equation. Further, the energy conditions are also discussed with conformal motions. Moreover, it is concluded that our inquired solutions are physically viable in [Formula: see text] gravity.

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Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution (DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.