Electrical Engineering

Hongyou Chen1 and Xiaodong Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1

1School of Electrical Engineering, Zhengzhou University of Science and Technology Zhengzhou 450000,China 

 

Received: July 15, 2020
Accepted: July 23, 2020
Publication Date: December 1, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202012_23(4).0019  


ABSTRACT

The level set model is an advanced method in image segmentation, which has a better effect in medical image segmentation. Feature fusion strategy has been widely introduced into the frame work to improve the processing performance of complex images such as high noise and chaotic texture. The traditional image segmentation methods have the problem of uneven image gray scale. Therefore, we propose a deep feature fusion method based on dark channel for medical image segmentation in this paper. For the region feature of the images, the saliency feature of medical image is introduced. A deep edge feature extraction method based on dark channel is proposed. Based on the fusion result of region and deep edge features, the proposed method introduces the distance regularization term to normalize the level set function to enhance the stability of the evolution of the level set function. The new model is applied to medical image segmentation, and the experimental results show that the new model achieves more robust segmentation results and higher segmentation efficiency.


Keywords: Medical image segmentation, Deep feature fusion, Dark channel, Level set function, Distance regularization term


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