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In the field of material growth, the quantum dot (QD) image analysis is a fundamental tool. The main challenge is that such study is used to be made by nonautomatic procedures which are time consuming and subjective. We aim to implement an algorithm of automatic analysis of the QDs images. In this frame, efficient QDs segmentation is prerequisite. In this paper, a fast and robust method for the visual segmentation of QDs image based on marker-controlled watershed transform is proposed. According to the foreground markers and the boundary of the coarse partition, the watershed transform is utilized to accurately separate QDs. A next process is then implemented to filter the possible attached substrates based on the area-height distribution of the extracted QDs. Finally, almost all the QDs can be accurately and robustly extracted and thus their properties can be measured. The experimental results show that the proposed approach gives a good tradeoff between the easy usability and efficiency, execution time, and segmentation quality.