Cellular Automata for Medical Image Processing

Cellular automata (CA) were introduced to provide a formal framework for investigating the behaviour of dynamic complex system in which time and space are discrete. They comprise an array of cells, where each cell can be in one of a finite number of possible states, which is updated synchronously in discrete time steps according to local transition rules (cell rules). A state of a cell at the next time step is determined by its neighboring cell’s current state. A substantial numbers of CA activities occurred in the 1970s with the introduction of artificial life. There were a number of distinguished papers and books to date has investigated artificial life (Raghawan, 1993; Langton, 1986, 1992; Pesavento, 1995; Rietman, 1993). Interest in important features of physics was spawned largely by Tommaso Toffoli. Spephen Wolfram was responsible for capturing the wider interest of the physics community with a series of papers in the 1980s, while others were applying CAs to a variety of problems in other fields (Sarkar & Abbasi, 2006; Xiao et al., 2008; Bandini et al., 2001; Mizas et al., 2008). In present, CA are being studied from many widely different angles, and the relationship of these structures to existing problems in being constantly sought and discovered (Reynaga & Amthauer, 2003; Mitchell et al., 1994; Hecker et al., 1999). As the topology of CA, they appear as natural tools for image processing due to their local nature and simple parallel computation implementation. To date, there are a number of papers which generally discuss cellular automata for image processing (Hernandez & Herrmann, 1996; Rosin, 2006; Chen & Horng, 2010; Chen & Lai, 2007; Eslami et al., 2010; Tzionas et al., 1997; Umeo, 2001). In this regard, there were some papers discuss medical image processing using CA model (Cheng et al, 2006; Viher et al, 1998, Ferrari et al., 2004; Chen et al., 2008). This paper presents a number of cellular automata-based algorithms for medical image processing. It starts by introducing cellular automata fundamentals necessary for understanding the proposed algorithms. Then, a number of cellular automata algorithms for medical image edge detection, noise filtering, spot detection, pectoral muscle identification and segmentation was presented. In this regard, 2-D mammogram images for the breast cancer diagnosis were investigated.