A Medical Image Segmentation Method With Anti-Noise and Bias-Field Correction

Abstract

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.

SM-SegNet: A Lightweight Squeeze M-SegNet for Tissue Segmentation in Brain MRI Scans

“…where the term ∩ refers to the overlap between the ground truth and the segmented map, while the |.| indicates the cardinality of the set. In addition, the maximum distances between points from one set and the nearest point from the other set using the Hausdorff distance (HD) were measured using (11).…”

“…To segment the object in an accurate and computationally efficient way, numerous machine learning approaches have been introduced, based on clustering algorithms [ 5 , 6 ], level set methods [ 7 , 8 ], and pattern recognition approaches [ 9 , 10 ]. However, the performance of these approaches is often hindered by brain structure complexity, low soft-tissue contrasts, non-uniform intensity, the partial volume effect, and MRI noise [ 11 ]. In addition, feature extraction does not produce accurate segmentation results of the brain MRI in the presence of image noise and other imaging artifacts.…”

Research on ARKFCM Algorithm Based on Membership Constraint and Bias Field Correction in Neonatal HIE Image Segmentation Method

“…If you can break the limit of the fuzzy index m value, you can improve the performance of the algorithm. e ARKFCM algorithm is optimized by improving the generalization problem of the fuzzy index m. From [13], we can see an improved GIFP_FCM algorithm based on the membership constraint function of the IFP-FCM [22] algorithm. e algorithm is based on competitive learning [23,24].…”

“…is method uses local heterogeneity information of the image, can adapt to local noise, can obtain clustering parameters in advance, and has less computational overhead. Xu et al [13] proposed an MR image segmentation method (MPCFCM) with antinoise and bias field correction. e MPCFCM algorithm can better correct the bias field, eliminate noise, and obtain more detailed and accurate image segmentation results.…”

A Novel Deep Learning Finger Vein Segmentation Algorithm for Identification and Recognition

“…Literature [12][13][14] are widely used in various types of medical image segmentation problems.V-Net [15] , FC-DenseNet [16] and Deeplab series networks [17][18][19] were originally proposed to solve the problem of natural image segmentation, and later were widely used in the field of medical image segmentation as the main network of segmentation. In addition, some traditional image segmentation algorithms [20][21] can also achieve good segmentation results.…”