###### Abstract
Due to its strong robustness and excellent solving ability, the Otsu method based on the above optimization algorithm has been expanded [29; 30; 31; 32; 33]. Accurate segmentation of medical images is a very difficult work problem due to the weaknesses of strong noise, complex tissue structure, blurred area boundaries, etc. To overcome this problem, [PERSON] et al. extended Otsu to become two-dimensional, which fully considers the spatial information of image pixels and their neighborhoods [34]. It can achieve ideal segmentation results even when the original images with low contrast and low signal-to-noise ratio. [PERSON] et al. proposed a 3D Otsu algorithm which comprehensively considers the original image, the neighborhood median and neighborhood mean information of the original image [35]. This method has stronger anti-noise performance and achieves better performance than 2D Otsu. [PERSON] et al. proposed a method which combines image neighborhood mean and neighborhood median to suppress noise. It can reduce the noise well in MR images and preserve the structure of medical images detail [36]. [PERSON] et al. proposed an iterative threshold segmentation method, which can segment weak objects and fine details well [37]. Inspired by this method, our team proposed a new interval iteration method and extended it to multiple thresholds [38]. However, the time complexity is too high. At present, there is no algorithm that can adapt to all images and achieve good segmentation results with low time complexity. For medical images, the structure is complex, the shape is irregular, and a lot of noise is easily generated during the acquisition process. In recent years, many scholars have proposed various methods to overcome these shortcomings, but medical image segmentation is still a challenging task [39; 40]. To improve the segmentation accuracy of medical images and reduce the time complexity, we propose a novel interval iterative multi-thresholding algorithm based on hybrid spatial filter and region growing. The proposed method fully considers the neighborhood information in original image. It reduces the impact of noise on the quality of image segmentation, and significantly reduces the time complexity. The contributions of this paper can be summarized as follows:1. A hybrid spatial filter is proposed to achieve image multi-scale decomposition which denoising while preserving more details. The proposed filter makes full use of the spatial information in the original image. It can improve the accuracy of image segmentation and make the algorithm more powerful and robust. 2. We proposed an interval iterative Otsu method based on region growing (RGIIM). It quickly obtains the growth threshold through the region growing method (RGM) and uses the idea of interval iteration to optimize the thresholds. This method achieves satisfactory segmentation results with minimal time cost. 3. A weighted strategy is used to fuse the segmentation result of the original image and its hybrid layers to make the final segmentation result more accurate. The rest of the paper is organized as follows: Section 2 details the interval iterative Otsu method based on region growing. Section 3 describes our proposed algorithm. Section 4 depicts segmentation results and analysis on MR brain images. Conclusions and future work are presented and discussed in Section 5.