Hepzibah A. Christinal

Segmentation in 2D and 3D Image Using Tissue-Like P System

Membrane Computing is a biologically inspired computational model. Its devices are called P systems and they perform computations by applying a finite set of rules in a synchronous, maximally parallel way. In this paper, we open a new research line: P systems are used in Computational Topology within the context of the Digital Image. We choose for this a variant of P systems, called tissue-like P systems , to obtain in a general maximally parallel manner the segmentation of 2D and 3D images in a constant number of steps. Finally, we use a software called Tissue Simulator to check these systems with some examples.

Region-based segmentation of 2D and 3D images with tissue-like P systems

Highlights? Membrane Computing is inspired by the structure and functioning of cells. ? We take a non-synchronous, distributed and parallel model. ? We present a membrane solution to segment 2D and 3D digital images. ? Our solution is logarithmic with respect to the input data. Membrane Computing is a biologically inspired computational model. Its devices are called P systems and they perform computations by applying a finite set of rules in a synchronous, maximally parallel way. In this paper, we develop a variant of P-system, called tissue-like P system in order to design in this computational setting, a region-based segmentation algorithm of 2D pixel-based and 3D voxel-based digital images. Concretely, we use 4-adjacency neighborhood relation between pixels in 2D and 6-adjacency neighborhood relation between voxel in 3D for segmenting digital images in a constant number of steps. Finally, specific software is used to check the validity of these systems with some simple examples.

P systems and computational algebraic topology

Membrane Computing is a paradigm inspired from biological cellular communication. Membrane computing devices are called P systems. In this paper we calculate some algebraic-topological information of 2D and 3D images in a general and parallel manner using P systems. First, we present a new way to obtain the homology groups of 2D digital images in time logarithmic with respect to the input data involving an improvement with respect to the algorithms development by S. Peltier et al. Second, we obtain an edge-segmentation of 2D and 3D digital images in constant time with respect to the input data.

Using Membrane Computing for Obtaining Homology Groups of Binary 2D Digital Images

Membrane Computing is a new paradigm inspired from cellular communication. Until now, P systems have been used in research areas like modeling chemical process, several ecosystems, etc. In this paper, we apply P systems to Computational Topology within the context of the Digital Image. We work with a variant of P systems called tissue-like P systems to calculate in a general maximally parallel manner the homology groups of 2D images. In fact, homology computation for binary pixel-based 2D digital images can be reduced to connected component labeling of white and black regions. Finally, we use a software called Tissue Simulator to show with some examples how these systems work.

Color Segmentation of 2D Images with Thresholding

Membrane Computing is a biologically inspired computational model. Its devices are called P systems and they perform computations by applying a finite set of rules in a synchronous, maximally parallel way. In this paper, we follow a new research line using tissue-like P systems to do a parallel color segmentation of images using a thresholding to look for edge pixels. We have chosen this variant of P systems because it uses a less number of computational ingredients with respect to classical variants.

Improved Parallelization of an Image Segmentation Bio-Inspired Algorithm

In this paper, we give a solution for the segmentation problem using membrane computing techniques. There is an important difference with respect to the solution presented in Christinal et al. [6], we use multiple membranes. Hence, the parallel behavior of the algorithm with respect to the previous works has been improved.

Using Membrane Computing for Effective Homology

Effective Homology is an algebraic-topological method based on the computational concept of chain homotopy equivalence on a cell complex. Using this algebraic data structure, Effective Homology gives answers to some important computability problems in Algebraic Topology. In a discrete context, Effective Homology can be seen as a combinatorial layer given by a forest graph structure spanning every cell of the complex. In this paper, by taking as input a pixel-based 2D binary object, we present a logarithmic-time uniform solution for describing a chain homotopy operator

Region-based segmentation of Hexagonal digital images using membrane computing

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