Download PDFOpen PDF in browser

Liver CT Image Processing And Diagnosing Using Artificial Neural Networks And MATLAB

10 pagesPublished: May 15, 2020

Abstract

Segmentation is a grand challenge, and there are many contests are held around the world to solve this challenge, especially in the biomedical image. There are many solutions to solve this challenge have been published.
Nowadays, neural networks, including deep learning is a powerful and state-of-the-art way to segment objects from the background. But to use deep learning effectively, besides design a good network architecture, the preparation of input data is also an important requirement. Active contour (another name: Snake) is a classical segmentation technique in image processing. But the accuracy of this technique is not as high as we need for health care problems, and soft techniques such as neural networks or deep learning can improve this problem. But in those researches, deep learning is supplied to change the parameters of the active contour algorithm.
We propose a combination of two fields of solving segmentation problem, a classical one, and a modern: using data from active contour to be the input of deep learning. The images to be used in this research are human liver CT images.

Keyphrases: active contour, Biomedical segmentation, CT human liver image, deep learning, neural network

In: Tich Thien Truong, Trung Nghia Tran, Quoc Khai Le and Thanh Nha Nguyen (editors). Proceedings of International Symposium on Applied Science 2019, vol 3, pages 79--88

Links:
BibTeX entry
@inproceedings{ISAS2019:Liver_CT_Image_Processing,
  author    = {Nhat Nguyen Thanh Minh and Van Hoang Tien Tran},
  title     = {Liver CT Image Processing And Diagnosing Using Artificial Neural Networks And MATLAB},
  booktitle = {Proceedings of International Symposium on Applied Science 2019},
  editor    = {Tich Thien Truong and Trung Nghia Tran and Quoc Khai Le and Thanh Nha Nguyen},
  series    = {Kalpa Publications in Engineering},
  volume    = {3},
  pages     = {79--88},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1770},
  url       = {https://easychair.org/publications/paper/6n6d},
  doi       = {10.29007/3dj7}}
Download PDFOpen PDF in browser