Abstract
Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.
Highlights
Magnetic resonance imaging (MRI) is a part and parcel of medical imaging for its virtues such as rapid acquirement, non-intrusive and painless [1]
A weighted level set model (WLSM) based on local kernel metric and spatial constraint is proposed to segment brain MRI images corrupted by noise and intensity inhomogeneity
It is used to segment MRI images with varying degrees of noise to demonstrate that the weighted neighborhood information embedded in WLSM can enhance the quality of MRI images and improve segmentation accuracy (SA)
Summary
Magnetic resonance imaging (MRI) is a part and parcel of medical imaging for its virtues such as rapid acquirement, non-intrusive and painless [1]. Zhang et al presented a level set evolution method driven by enhanced term to improve the effectiveness of DRLSE [39], the optimized area energy term was defined in their study to detect the boundaries of an image with several disjoint targets. Region-based models have more advantages than edge-based models since they use region information rather than gradient information to drive the motion of level set function [40] They can be used to segment the images with weak boundaries and low contrast, and it is often insensitive to the position of the initial contour. A weighted level set model (WLSM) based on local kernel metric and spatial constraint is proposed to segment brain MRI images corrupted by noise and intensity inhomogeneity.
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