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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1264-1269

Medical image segmentation network based on convolution capsule encoder and multi-scale local feature co-occurrence

Qin Chendong
Wang Yongxiong
Zhang Jiapeng
School of Opto-Electronic Information & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0352

Abstract

U-Net has achieved great success in the field of image segmentation. However, some of the position information is lost in the process of convolution and downsampling, model is difficult to learn global and long-range semantic interaction information and lacks the ability to integrate global and local information. To extract rich local detail and contextual information, this paper proposed an image segmentation network called MLFCNet, combining a convolutional module and a capsule encoder. Based on the U-Net, this paper introduced a capsule network module to learn target positional information and the relationships between local and global information. At the same time, the proposed attention mechanism could retain the information discarded by the network pooling layer. This paper designed a new attention mechanism so that multi-scale features could be fused, where global information was captured and background noise was suppressed. In addition, it proposed a new local feature co-occurrence algorithm to better learn the relationship between local features. The proposed method was compared with nine methods on two public datasets, mIoU improves 4.7% and Dice coefficient improves 1.7% in liver medical images compared to the second highest performing model. Experimental results on the dataset of liver and dataset of human show that under the same experimental conditions, the proposed network is superior to U-Net and other mainstream image segmentation networks.

Foundation Support

上海市自然科学基金资助项目(22ZR1443700)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0352
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Technology of Graphic & Image
Pages: 1264-1269
Serial Number: 1001-3695(2024)04-045-1264-06

Publish History

[2023-11-01] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

秦辰栋, 王永雄, 张佳鹏. 基于卷积胶囊编码器和多尺度局部特征共现的图像分割网络 [J]. 计算机应用研究, 2024, 41 (4): 1264-1269. (Qin Chendong, Wang Yongxiong, Zhang Jiapeng. Medical image segmentation network based on convolution capsule encoder and multi-scale local feature co-occurrence [J]. Application Research of Computers, 2024, 41 (4): 1264-1269. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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