Abstract
In the diagnosis of clinical medicine, medical image processing plays a vital role and has become a hot issue in image processing. Magnetic resonance imaging not only provides convenience for treatment, but also brings help to the rehabilitation of patients. However, there are some unfavorable factors in MRI brain images, such as blurred boundary data, weak anti-noise ability, and so on. The classical fuzzy clustering algorithm has strong advantages, but the improved method is relatively simple, only adjusting the degree of membership or changing the distance algorithm to enhance the clustering effect. Therefore, this paper proposes a new multitask quadratic regularized clustering (MT-QRC) algorithm for MRI brain image segmentation, which improves the single-task clustering performance by transferring relevant knowledge between tasks. The proposed MT-QRC algorithm introduces the spatial information item controlled by the quadratic regularization term to replace the fuzzy index, which reduces the limitation of the fuzzy index in clustering and enhances the parameter flexibility.
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