###### Abstract  
Medical image segmentation is widely used in clinical medicine, and the accuracy of the segmentation algorithm will affect the diagnosis results and treatment plans. However, manual segmentation of medical images requires extensive experience and knowledge, and it is both time-consuming and labor-intensive. To overcome the problems above, we propose a novel interval iterative multi-thresholding segmentation algorithm based on hybrid spatial filter and region growing for medical brain MR images. First, a hybrid spatial filter is designed to perform on the original image, which can make full use of the spatial information while denoising. Second, the interval iterative Otsu method based on region growing is proposed to segment the original image and its filtering layer. The initial thresholds can be quickly obtained by region growing algorithm, which can reduce the time complexity. The interval iterative algorithm is used to optimize the thresholds. Finally, a weighted strategy is used to refine the segmentation results. The segmentation results of our proposed algorithm outperform other comparison algorithms in both subjective and objective evaluations. Subjectively, the obtained segmentation results have clear edges, complete and consistent regions. We use the uniformity measure (\(U\)) for objective evaluation, and the \(U\) value is significantly higher than other comparison algorithms. The proposed algorithm achieved an average \(U\) value of 0.9854 across all test images. The proposed algorithm can segment medical images well and expand the doctor's ability to utilize medical images. multi-threshold; region growing; interval iteration; medical image segmentation Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON]. simplicity and efficiency, and the most classic algorithms include the Otsu method [17], the maximum entropy method [18], and the minimum error method [19]. The [PERSON]'s thresholding method is a well-known method for medical images segmentation [20]. The key to the threshold segmentation method is the selection of the threshold, which directly determines the quality of image segmentation. The threshold segmentation method is divided into single threshold segmentation and multi-threshold segmentation. Single threshold segmentation is to divide the image into two regions: foreground and background [21]. However, a single threshold segmentation can no longer meet clinical medical needs because of the complexity of medical images. For example, an MR (magnetic resonance) brain image can be divided into five regions in general, including gray matter, white matter, cerebrospinal fluid, skull, and background. Therefore, it is necessary to expand single threshold segmentation to multi-threshold segmentation [22]. When single threshold segmentation is extended to multi-threshold segmentation, it often causes a huge increase in time complexity and a decrease in segmentation accuracy. Therefore, how to propose a fast image segmentation method with high segmentation quality and accuracy is still a challenging task. To solve the problem of high time complexity, [PERSON] proposed a method of merging histogram regions to find the thresholds [23]. The optimal solution can be obtained by reducing the histogram region number in iterations. This method reduces the time complexity, but the segmentation accuracy is not high enough. Swarm intelligence algorithms can find optimal solutions in the solution space [24; 25]. These methods can overcome the shortcomings of the exhaustive work. Therefore, combining image segmentation algorithms with swarm intelligence algorithms has become a research hotspot. The swarm intelligence algorithms include genetic algorithm (GA), ant colony algorithm (ACA), particle swarm algorithm (PSO), sparrow search algorithm (SSA), wolf swarm algorithm (WSA), Bacterial Foraging Algorithm (BF), adaptive bacterial foraging algorithm (ABF), Real coded Genetic Algorithm (RCGA), nelder-mead simplex method (NMS), etc. [26; 27; 28]. These algorithms are classic optimization algorithms.