Brain magnetic resonance images (MRI) are affected by noise and bias field, which make the traditional FCM algorithm unable to segment tissue regions of MR images accurately. Based on the above problems, this paper proposes an MR image segmentation method (MPCFCM) with anti-noise and bias field correction, which implements segmentation by point-to-plane algebraic distance constraint. Different from traditional point-based clustering methods, a hyper-center of clustering (i.e., plane) model is defined, and data clustering is completed by optimizing different planes. In addition, to realize the point clustering with plane, a key problem that how to measure the distance from point to plane needs to be solved. This paper adopts the algebraic distance as a measure function, which can avoid the nonlinear problem caused by a direct calculation of the minimum distance between a point and a plane, thus simplifying the computational complexity. In the proposed algorithm, spatial distance, local variance and gray-difference of neighbors are combined to construct a new anti-noise smoothing factor for constraining the energy function so that the algorithm has better anti-noise and retains more image details. Finally, the singular value decomposition is performed on the loss energy, some information removed is re-added to the segmented image to repair it. The experimental results show that MPCFCM algorithm can better correct bias field and eliminate noise and obtain accurate image segmentation results with more details. INDEX TERMS Bias field correction, fitting plane, algebraic distance, anti-noise smoothing factor.

With the comprehensive development of the "Double High Plan" construction in higher vocational colleges, teaching organization management plays an important role in promoting the high-quality development of higher vocational education. However, there are inevitably problems in higher vocational colleges that the teaching management system is not perfect and the teaching management operation mechanism is not optimized enough, which hinders the high-quality development of higher vocational colleges to a certain extent. In order to better solve the above problems, this paper proposes a new teaching organization and management system, guided by constructivism and multimedia learning cognition theory, through the digitalization and intelligent innovation of the teaching organization and management system, in order to further optimize resource allocation, Improve governance capabilities.

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Cross-age image generation technology is to generate cross-age face images on the basis of the original face image. The synthetic face image can show facial details such as skin, wrinkles and hair at a certain age. The technology can be widely used in film and television, animation, public safety and other fields. Cross-age face synthesis techniques can be divided into traditional cross-age face synthesis techniques and cross-age face synthesis techniques based on generative adversarial network models. With the continuous development of GAN, the technologies based on generative adversarial network models have made more progress and advantages in the field of face synthesis. The model in this paper, based on the generation of the adversarial network model, combines the advantages of the conditional autoencoder and the StyleGAN model, and innovates in the use of the feature contrasting device, which can generate HD face images consistent with the change logic across ages, and effectively avoid the emergence of problems such as organ deformation and identity inconsistency.

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