Hongyou Chen1 and Zengyong Xu This email address is being protected from spambots. You need JavaScript enabled to view it.2

1School of Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou 450000,China
2Henan College of Transportation, Zhengzhou 450000,China


 

Received: September 20, 2020
Accepted: October 27, 2020
Publication Date: April 1, 2021

 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.202104_24(2).0009  


ABSTRACT


Medical image segmentation is a necessary step to assist disease diagnosis. Due to fuzzy boundary and low contrast of human organs, medical image automatic segmentation is still a difficult problem. Aiming at the problem of poor accuracy by using the traditional fully convolutional neural network (FCN), this paper proposes a new end-to-end network model for medical image segmentation. Firstly, linear pixel value transformation is used to adjust the brightness and contrast of the original data. Then the histogram equalization is used to remove the noise and keep the main details of the image. Then the proposed FCN network is trained by using the processed data set. Finally, we make comparison with other state-of-the-art segment methods, the results show that our proposed has better segmentation effect and it can provide reliable evidence for clinical diagnosis.


Keywords: Medical image segmentation; FCN; histogram equalization; linear pixel value transformation


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