Region-growing (RG) algorithm is one of the most common image segmentation methods used for different image processing and machine vision applications. However, this algorithm has two main problems: (1) high computational complexity and the difficulty of its parallel implementation caused by sequential process of adding pixels to regions; (2) low performance of RG in region with weak edges, due to the use of location and the number of seed points. In this paper, a new model of RG algorithm based on tissue-like P system is proposed to resolve these limitations. In this model, each pixel is modeled by a membrane, and in one step, the similarity of each membrane with its neighbors is computed. Then, all membranes are used as seed points to grow simultaneously in a parallel and flood-like manner. To realize the parallel implementation of the proposed model, Graphic Processing Unit (GPU) and CUDA programming language are used. The evaluation of execution time indicates that the proposed model has better performance than the conventional RG algorithm, its speed-up is about 12.5×. Qualitative and quantitative evaluations of segmentation performance also demonstrate that the proposed method not only does not damage the overall segmentation accuracy, but also it has better results on images with complicated background compared to the state-of-the-art methods.

区域增长（RG）算法是用于不同图像处理和机器视觉应用的最常见图像分割方法之一。但是，该算法存在两个主要问题：（1）计算复杂度高，并且由于将像素相加到区域的顺序过程而导致并行实现困难。（2）由于使用位置和种子点的数量，RG在边缘较弱的区域中的性能较低。为了解决这些局限性，提出了一种基于类组织P系统的RG算法新模型。在此模型中，每个像素都由一个膜建模，并且一步就可以计算出每个膜与其相邻像素的相似度。然后，将所有膜用作种子点，以平行且泛洪的方式同时生长。为了实现所提出模型的并行实现，使用了图形处理单元（GPU）和CUDA编程语言。对执行时间的评估表明，所提出的模型比传统的RG算法具有更好的性能，其提速约为12.5倍。分割性能的定性和定量评估还表明，与最新方法相比，该方法不仅不会损害整体分割精度，而且在背景复杂的图像上具有更好的效果。