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.
