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  


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


  1. [1] M. Sugimoto, N. Taniguchi, and K. Shinjo. Medical Image Processing in Clinical Extended Reality (Virtual Reality, Augmented Reality, Mixed Reality) and Automatic Segmentation of The Internal Organs Using Artificial Intelligence and Deep Learning. Medical Imaging Technology, 37, 2019.
  2. [2] Lin Teng, Hang Li, and Shahid Karim. DMCNN: A Deep Multiscale Convolutional Neural Network Model for Medical Image Segmentation. Journal of Healthcare Engineering, 2019.
  3. [3] X. Zhang, D. Wang, and H. Chen. Improved Biogeography-Based Optimization Algorithm and Its Application to Clustering Optimization and Medical Image Segmentation. IEEE Access, 7:28810–28825, 2019.
  4. [4] Y. Shoulin, Z. Ye, and K. Shahid. Large Scale Remote Sensing Image Segmentation Based on Fuzzy Region Competition and Gaussian Mixture Model. IEEE Access, 6:26069–26080, 2018.
  5. [5] Shoulin Yin and Jing Bi. Medical image annotation based on deep transfer learning. Journal of Applied Science and Engineering, 22(2):385–390, 2019.
  6. [6] Getao Du, Xu Cao, Jimin Liang, Xueli Chen, and Yonghua Zhan. Medical image segmentation based on U-Net: A review. Journal of Imaging Science and Technology, 64(2), mar 2020.
  7. [7] Guang Zhang, Shuhao Dong, Hui Xu, Hongyu Zhang, Yongjian Wu, Yanwei Zhang, Xiaoming Xi, and Yilong Yin. Correction Learning for Medical Image Segmentation. IEEE Access, 7:143597–143607, 2019.
  8. [8] Shoulin Yin, Lei Meng, and Jie Liu. A new apple segmentation and recognition method based on modified fuzzy C-means and hough transform. Journal of Applied Science and Engineering, 22(2):349–354, 2019.
  9. [9] Lin Teng, Hang Li, Shoulin Yin, and Yang Sun. Improved krill group-based region growing algorithm for image segmentation, oct 2019.
  10. [10] Lin Teng, Hang Li, Shoulin Yin, Shahid Karim, and Yang Sun. An active contour model based on hybrid energy and fisher criterion for image segmentation, jan 2020.
  11. [11] Shoulin Yin and Hang Li. GSAPSO-MQC:medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system. Evolutionary Intelligence, 2020.
  12. [12] Jialiang Wang, Jianxu Luo, Bin Liu, Rui Feng, Lina Lu, and Haidong Zou. Automated diabetic retinopathy grading and lesion detection based on the modified R-FCN object-detection algorithm. IET Computer Vision, 14(1):1–8, feb 2020.
  13. [13] Xiaowei Wang, Shoulin Yin, Ke Sun, Hang Li, Jie Liu, and Shahid Karim. GKFC-CNN: Modified Gaussian Kernel Fuzzy C-means and Convolutional Neural Network for Apple Segmentation and Recognition. Journal of Applied Science and Engineering, 23(3):555–561, 2020.
  14. [14] Javier Civit-Masot, Francisco Luna-Perejon, Saturnino Vicente-Diaz, Jose Maria Rodriguez Corral, and Anton Civit. TPU cloud-based generalized U-Net for eye fundus image segmentation. IEEE Access, 7:142379–142387, 2019.
  15. [15] Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. SegNet: A Deep Convolutional EncoderDecoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481–2495, dec 2017.

Latest Articles


27th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.