Multimed Tools Appl 79:17045–17056Ĭastiglione A, Santis AD, Pizzolante R, Castiglione A, Loia V, Palmieri F (2015) On the protection of fMRI images in multi-domain environments. J Digit Imaging 32(1):162–174īuvanesvari VK, Suganthi M (2020) Three dimensional modelling of MRI knee images using improved edge detection and finite element modelling. īahar K, Mehran Y (2018) A new optimized thresholding method using ant colony algorithm for MR brain image segmentation. IET Image Process 12(11):1964–1971Īllioui H, Sadgal M, Elfazziki A (2021) Optimized control for medical image segmentation: improved multi-agent systems agreements using Particle Swarm Optimization. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation.Īdel K, Khaled A, Ferhat Z (2018) Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The image is segmented by T 2 to improve the accuracy of image segmentation. The hierarchical threshold model is adopted: the macro-threshold T 1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T 2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T 1. On the basis of bilateral filtering, the method uses Sigmoid function to identify the noise and adaptively calculate the weight of neighborhood pixel, and then constructs a 2D histogram of gray value-adaptive weight neighborhood gray mean to enhance the algorithm’s anti-noise capability and detail retention. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration.
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