对医学图像进行分割可以帮助临床医师对相应病灶进行更为深刻的分析和研究，本文以典型CT图像为例，探究了在Matlab软件下进行医学图像预处理与分割的基本方法。在医学图像分割上分别阐述和比较了以Otsu方法为代表的阈值分割、以一阶和二阶微分算子为代表的边缘检测分割和以分水岭方法为代表的区域分割方法。结果显示，传统的分割技术依然有着算法简单、占用内存低、耗时短等优势，在实际的分割精度要求不是很高时可以更为快捷地得到结果。

Medical image segmentation can help doctors analyze and study the corresponding diseases. Therefore, this paper takes typical CT images as an example to explore the basic methods of medical image preprocessing and segmentation under Matlab software. In medical image segmentation, the threshold segmentation represented by Otsu method, the edge detection segmentation represented by first-order and second-order differential operator and the region segmentation represented by watershed method are described and compared respectively. The results show that the traditional segmentation technology still has the advantages of simple algorithm, low memory consumption and short processing time. It can get results more quickly when the actual segmentation accuracy is not very high.