Computer Science ›› 2018, Vol. 45 ›› Issue (3): 283-287.doi: 10.11896/j.issn.1002-137X.2018.03.046

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Image Segmentation Method of Level Set Regularization Based on Bessel Filter

LIU Guo-qi and LI Chen-jing   

  • Online:2018-03-15 Published:2018-11-13

Abstract: A new regularization method based on Bessel filter was proposed to solve the problem of numerical stability of the level set function in the evolutionary process.A new energy model was constructed by embedding this method into the classical region-scalable fitting(RSF) model.Firstly,the K-means algorithm is used to generate the initial level set function automatically to solve the problem of the initialization sensitivity of the RSF model.Secondly,the advantages of region-scalable fitting model are used for iterative segmentation.Finally,in the iterative process,the proposed method is used to maintain the stability of the level set function in order to achieve accurate segmentation results.The experimental results show that the proposed regularization method effectively preserves the stability of the level set functions.The new model has higher efficiency and segmentation accuracy compared with other models based on region.

Key words: Level set regularization,Level set evolution,Bessel filter,Region-scalable fitting model,K-means

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