This paper presents a method that combines graph-based segmentation and multistage region merging to segment laparoscopic images. Starting with image pre-processing, including Gaussian smoothing, brightness and contrast enhancement, and histogram thresholding, we then apply an efficient graph-based method to produce a coarse segmentation of laparoscopic images. Next, regions are further merged in a multistage process based on features like grey-level similarity, region size and common edge length. At each stage, regions are merged iteratively according to a merging score until convergence. Experimental results show that our approach can achieve good spatial coherence, accurate edge location and appropriately segmented regions in real surgical images.