Automatic 2d image segmentation using tissue-like p system

This paper uses P-Lingua, a standard programming language that is designed specifically for P systems, to automatically simulate the region-based segmentation of 2D images. P-Lingua, which is based on membrane computing, links to Java Netbeans using the PLinguaCore4 Java library to automatically codify the pixels of the input image as long as automatically draw the output segmented image. Many methods have been suggested previously and used for artificial image segmentation, but to the best of our knowledge, none of those techniques were automatic, where the image was codified manually and the visualization of the output image was done manually in the tissue simulator which takes time and effort, especially when dealing with large images in the system. Two types of pixel adjacency have been utilized in this paper, namely; 4-adjacency and 8-adjacency. The jacquard index method has been used to measure the accuracy of the segmentation. The results of the proposed method demonstrated that beside its ability to automatically segmenting 2D images with arbitrary sizes, it is more efficient and faster than the tissue simulator tool, since the latter needs the input image to be codified manually pixel by pixel which makes it impractical for real-world applications

Tissue-like P system for Segmentation of 2D Hexagonal Images

Membrane computing, which is a new computational model inspired by the structure and functioning of biological cells and by the way the cells are organized in tissues. MC has been adopted in many real world applications including image segmentation. In contrast to the traditional square grid for representing and sampling digital images, hexagonal grid is an alternative efficient mechanism which can better represents and visualizes the curved objects. In this paper, a tissue-like P system with region-based and edge-based segmentation is used to segment two dimensional hexagonal images, wherein P-Lingua programming language is used to implement and validate the proposed system. The achieved experimental results clearly demonstrated the effectiveness of using hexagonal connectivity to segment two dimensional images in a less number of rules and computational steps. Moreover, the results reveal that this approach has the potential of segmenting large images in few number of steps

Membrane Computing for Real Medical Image Segmentation

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels of 8-adjacency give better results compared with the 4-adjacency neighborhood relations, because the 8-adjacency considers the eight pixels around the center pixel, which reduces the required communication rules to obtain the final segmentation results. The experimental results proved that the proposed approach has superior results in terms of the number of computational steps and processing time. To the best of our knowledge, this is the first time an evaluation procedure is conducted to evaluate the efficiency of real image segmentations using membrane computing

A Spectral Clustering Algorithm Improved by P Systems

Using spectral clustering algorithm is diffcult to find the clusters in the cases that dataset has a large difference in density and its clustering effect depends on the selection of initial centers. To overcome the shortcomings, we propose a novel spectral clustering algorithm based on membrane computing framework, called MSC algorithm, whose idea is to use membrane clustering algorithm to realize the clustering component in spectral clustering. A tissue-like P system is used as its computing framework, where each object in cells denotes a set of cluster centers and velocity-location model is used as the evolution rules. Under the control of evolutioncommunication mechanism, the tissue-like P system can obtain a good clustering partition for each dataset. The proposed spectral clustering algorithm is evaluated on three artiffcial datasets and ten UCI datasets, and it is further compared with classical spectral clustering algorithms. The comparison results demonstrate the advantage of the proposed spectral clustering algorithm

Tissue-like p system for region-based and edge-based image segmentations

Membrane Computing (MC), a relatively recent branch of natural computing is an emerging field in molecular computing. MC aims at abstracting models, called membrane systems or P systems, which mimic the function and structure of a biological cell. Many studies have utilized MC in various applications such as image segmentation. Due to the high computational cost of conventional segmentation techniques, bio-inspired models including MC may be applicable to tackle this limitation. In this study, tissue-like P systems, a variant of MC, with sophisticated communication rules were developed to improve regionbased and edge-based segmentation algorithms for manual and automatic segmenting of 2D artificial and real images. Manual segmentation was applied for artificial images, whereas, the automatic segmentation was applied for artificial and real medical images. The manual segmentation of 2D artificial images was achieved using four, six and eight adjacency pixel connectivity relationships, whereas, the automatic segmentation of 2D artificial and real medical images were achieved using four and eight adjacency pixel connectivity relationships. Two methods were used to realize the automatic edge-based and region-based segmentations. The first method is for 2D artificial images using P-lingua linked to Java Netbeans using the P-linguaCore4 Java Library. The second method is for the 2D real and real medical images using C# linked to P-linguaCore4 Java library. The results of the second method demonstrated the ability of the system to automatically segment 2D real and real medical images with arbitrary sizes and different image formats. The experimental results statistically proved that the methods markedly outpaced the state-of-the-art methods of 2D real image segmentation using the same data set. Furthermore, the methods showed better segmentation accuracy and ability to deal with images of different sizes and types. Extra efficient results such as reducing the number of rules and computational steps were achieved for 2D hexagonal artificial images based on Tissue-like P systems. The main contributions of this study are automatic loading and codifying of the input image as well as automatic visualization of output images after segmentation. Furthermore, six and eight adjacency pixel connectivity relationships should be considered for reducing computational steps, number of rules used and processing time in molecular computing

Optimal multi-level thresholding with membrane computing

The conventional methods are not effective and efficient for image multi-level thresholding due to time-consuming and expensive computation cost. The multi-level thresholding problem can be posed as anoptimization problem, optimizing some thresholding criterion. In this paper, membrane computing isintroduced to propose an efficient and robust multi-level thresholding method, where a cell-like P systemwith the nested structure of three layers is designed as its computing framework. Moreover, an improvedvelocity-position model is developed to evolve the objects in membranes based on the special membranestructure and communication mechanism of objects. Under the control of evolution-communicationmechanism of objects, the cell-like P system can efficiently exploit the best multi-level thresholds for animage. Simulation experiments on nine standard images compare the proposed multi-level thresholdingmethod with several state-of-the-art multi-level thresholding methods and demonstrate its superiority.National Natural Science Foundation of China No. 61170030Chunhui Project Foundation of the Education Department of China No. Z2012025Chunhui Project Foundation of the Education Department of China No. Z2012031Research Fund of Sichuan Key Technology Research and Development Program No. 2013GZX0155Open Research Funds of Key Laboratory of High Performance Scientific Computing No. SZJJ2012-00