Open AccessLiver CT Image Processing And Diagnosing Using Artificial Neural Networks And MATLAB
Author(s) -
N. Minh,
Van Hoang Tien Tran
Publication year - 2020
Publication title -
kalpa publications in engineering
Language(s) - English
Resource type - Conference proceedings
ISSN - 2515-1770
DOI - 10.29007/3dj7
Subject(s) - artificial intelligence , deep learning , computer science , artificial neural network , segmentation , image segmentation , active contour model , recurrent neural network , image processing , matlab , image (mathematics) , medical imaging , pattern recognition (psychology) , computer vision , machine learning , operating system
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.