Adaptive trapezoid region intercept histogram based Otsu method for brain MR image segmentation

In brain magnetic resonance (MR) image segmentation, the current Otsu method is often difficult to take both accuracy and anti-noise capability into consideration. So, in this paper, an adaptive trapezoid region intercept histogram based Otsu method is proposed. 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. The hierarchical threshold model is adopted: the macro-threshold T1 is determined by the trapezoid region intercept histogram based Otsu method, and the micro-threshold T2 is determined by the between-class variance criterion again in the trapezoid region corresponding to T1. The image is segmented by T2 to improve the accuracy of image segmentation. Based on the neighborhood information, an adaptive parameter l is designed to identify and correct noise, thus enhancing the universality of the algorithm. The experimental results show that the proposed method is effective and can be well applied to MR image segmentation.