Hypergraph-Based Numerical Neural-Like P Systems for Medical Image Segmentation

Neural-like P systems are membrane computing models inspired by natural computing and are viewed as third-generation neural network models. Although real neurons have complex structures, classical neural-like P systems simplify the structures and corresponding mechanisms to two-dimensional graphs or tree-based firing and forgetting communications, which limit the real applications of these models. In this paper, we propose a hypergraph-based numerical neural-like (HNN) P system containing five types of neurons to describe the high-order correlations among neuron structures. Three new kinds of communication mechanisms among neurons are also proposed to address numerical variables and functions. Based on the new neural-like P system, a tumor/organ segmentation model for medical images is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods based on two hippocampal datasets and a multiple brain metastases dataset, thus verifying the effectiveness of the HNN P system in correctly segmenting tumors/organs.

Automated summary

We propose a hypergraph-based numerical neural-like (HNN) P system, which contains five types of neurons to capture the high-order correlations among neuron structures. Additionally, three new communication mechanisms are introduced to handle numerical variables and functions. Experimental results on medical image segmentation demonstrate that the proposed HNN P system outperforms existing methods, showing its effectiveness in accurately segmenting tumors/organs.