https://doi.org/10.1140/epjs/s11734-022-00474-0
Regular Article
A memristive RBF neural network and its application in unsupervised medical image segmentation
(EPJ ST Special Issue: Complex Bio Rhythms)*
1
School of Life Science, University of Electronic Science and Technology of China, 611731, Chengdu, China
2
School of Artificial Intelligence, Nanjing University of Information Science and Technology, 210044, Nanjing, China
3
School of Artificial intelligence and Engineering, Jiangsu Vocational Institute of Commerce, 210045, Nanjing, China
4
School of Electrical and Automation Engineering, East China Jiaotong University, 330013, Nanchang, China
Received:
3
August
2021
Accepted:
3
February
2022
Published online:
7
March
2022
Recently, the image segmentation algorithm based on neural network has made great progress in the field of medical image segmentation, but it still faces many challenges such as the small set of training sample data, the lackness of background training data, weak network generalization ability, and poor network performance. To overcome the above difficulties, a new memristive Radial Basis Function (RBF) neural network is proposed for training effectively small sample set, which shows strong power of global searching and great generalization ability. Based on the memristive neural network and K-means clustering algorithm, a new algorithm for unsupervised image segmentation is designed. Through the analysis of experimental data, it is found that the algorithm proposed in this paper can segment medical images accurately without expert labeling data.
© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2022
