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Most existing stroke-based graph cut image segmentation techniques use only intensity information of strokes to update intensity distributions of object and background. Accordingly, fluctuation effect may occur unexpectedly as a result of the global effect of regional term in the graph cut framework. In this note we present an iterative graph cuts-based image segmentation technique which incorporates local constraints. A new energy function with local constraints term generated following additional seed points is formulated and is minimized to obtain a globally optimal segmentation. We tested the method on cone-beam CT data of printed circuit board and comparatively found the strength of the proposed method in accuracy and controllability.

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Probabilistic atlases based on human anatomy structure have been widely used for organ segmentation, which is used as a prior probability in a Bayes framework. The challenge is how to register the probabilistic atlas to the patient volume. Taking these into consideration, we propose a template matching framework based on the probabilistic atlas for spleen segmentation. Firstly, we find a bounding box of the spleen based on human anatomical localization, which is the statistical geometric location of spleens. Then, the probabilistic atlas is used as a template to find the spleen in this bounding box by using template matching technology. We apply our method into 60 datasets including normal and pathological cases. The Dice/Tanimoto volume overlaps are 0.922/0.857, the root-meansquared error (RMSE) is 1.992 mm. The algorithm is robust to segment normal and abnormal spleens, such as the presence of tumors and large morphological changes. Meanwhile, our proposed method was compared with conventional atlas-based methods. Results demonstrate that segmentation accuracy improved using our method.