| Membrane computing (P system) abstracts computing models from the architecture and the functioning of living cells as well as from the organization of cells in tissues and organs. P system that possesses distributed, maximum parallel and non-deterministic computing features, and membrane algorithm that has a better adaptability and strong optimization ability, have advantages for solving complex optimization problems. How to apply these features and advantages to image processing is of great significance for both extending the application range of P system and developing new image processing algorithms. This paper focuses on the application of P system to image segmentation.Based on the mechanism of tissue-like P system and its parallel computing advantages, this paper discusses the single-threshold image segmentation and multi-threshold image segmentation by combining classic maximum between-class variance (Otsu), entropy method (KSW) principle and P system. The main innovations are shown as follows:(1) Based on tissue-like P system, a single-threshold based image segmentation method is proposed. We design a tissue-like P system with two layer membranes. Combining Otsu’s criterion and Ksw’s criterion, the P system searches the best segmentation threshold by the evolution rules and communication rules. We compare with traditional thresholding method and thresholding method based on genetic algorithms. The comparison results demonstrate the effectiveness and feasibility of the proposed thresholding method.(2)A multi-threshold image segmentation method based on tissue-like P system is proposed. According to the mechanism of the tissue-like P system and the principle of multi-threshold image segmentation, P system searches the best multi-threshold for multi-threshold image segmentation. By comparing with traditional thresholding method and thresholding method based on genetic algorithms, the effectiveness and feasibility of the proposed thresholding method are verified. |
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