Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (4): 96-102.doi: 10.11896/jsjkx.220300054

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Skin Lesion Segmentation Combining Boundary Enhancement and Multi-scale Attention

BAI Xuefei1, JIN Zhichao1, WANG Wenjian1,2, MA Yanan1   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education(Shanxi University),Taiyuan 030006,China
  • Received:2022-03-07 Revised:2022-08-16 Online:2023-04-15 Published:2023-04-06
  • About author:BAI Xuefei,born in 1980,Ph.D,asso-ciate professor,is a member of China Computer Federation.Her main research interests include image proces-sing and machine learning.
    WANG Wenjian,born in 1968,Ph.D,professor,is a member of China Computer Federation.Her main research interests include image processing,machine learning and computing intelligence.
  • Supported by:
    National Natural Science Foundation of China(61703252,62076154,62276161,U21A20513) and Key Research and Development Program of Shanxi Province(202102150401013).

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Abstract: In view of the various types of skin lesions in shape,color and size,which pose a huge challenge to the accurate segmentation of skin lesions,a skin lesion segmentation network that combines boundary enhancement and multi-scale attention is proposed(BEMA U-Net).It consists of two modules,one is called spatial multi-scale attention module,which is used to extract spatial global features,and the other is called boundary enhancement module,which is used to enhance the edge features of the lesion area.BEMA U-Net adds the two modules to the U-Net network with encoding and decoding structure,which can effectively suppress the interference of background noise in the image of lesions and enhance the edge details of lesions.In addition,the mixed loss function is designed,Dice loss and Boundary loss are combined,and the dynamic weight adjustment of the mixed loss function is realized in the training process,so that the network could carry out multiple supervision on the extraction of the overall features and edge details of the pathological images,and the problems of hair interference and edge blur in the segmentation of skin pathological images are alleviated.Experimental results on ISIC2017 and ISIC2018 public data sets show that the proposed algorithm has better segmentation effect for skin lesions with continuous edges and clear contours.

Key words: Skin lesion segmentation, Spatial multi-scale attention, Global feature, Boundary enhancement, U-Net

CLC Number: 

  • TP391
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