An improved threshold method based on histogram entropy for the blood vessel segmentation

Interest is growing in the interventional surgery training system used before the treatment of vessel diseases. As one of the elementary component of the simulator, an accurate reconstruction of blood vessel obtaining from cross-sectional images is ugly needed. Digital Subtraction Angiography (DSA) data is used as a criterion for reconstruction result of blood vessel. In this paper, an improved threshold method is proposed to segment blood vessel from medical images. Firstly, with optimization characteristic, the genetic algorithm is used to determine the pre-segmentation greyscale from best histogram entropy. Secondly, we enlarge greyscale around the best greyscale to adjust image intensity value which can greatly singularize the region of interest. And then, classical threshold method (Otsu algorithm) is used to determine the best threshold value which can separate blood vessel from background. To test the improved method, comparative trial was set to testify the improvement. Finally, a series of DSA images were obtained to demonstrate this method and the final experimental results showed the effectiveness of the improved method.

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