The image of the tuna before entering process classification, it must have a good segmentation results. The result of good segmentation is object and background separate clearly. The image of tuna which has a distribution of light that is uneven and has a complex texture will produce an error segmentation. One method of image segmentation was seeded region growing and parameters that used only two, namely seed and threshold. This research proposed method seeded region growing in the HSI color space for image segmentation of tuna. The Color space of RGB (red green blue) on image of tuna transformed into a color space HSI (hue saturation intensity) then only the hue color space used as segmentation by using seeded region growing. Determination of seed and threshold parameters can do manually and the result of the segmentation do refinement with mathematical morphology. The experiment using 30 image of tuna to segmentation and evaluation methods using RAE (relative foreground area error), MAE (missclassification error) and the MHD (modified Hausdroff distance). The image of the tuna successfully performed segmentation evidenced by a value RAE, ME and MHD respectively are 5,40%, 1,53% dan 0,41%.