Segmentation in Microscopic Images using Cellular Automata.

What is it about?

This paper investigates cellular automaton (CA)-based segmentation techniques on microscopic images. An image is considered a 2D cellular automaton, and the pixel values are considered the states of the cells. For updating the state of the cell, von Neumann and Moore neighbourhoods have been employed. An existing dataset of microscopic images has been taken, and various segmentation rules have been used for analysis. Additionally, a comparison has been made among the methods, with results demonstrating satisfactory performance. It is also observed that the von Neumann neighbourhood-based approach is producing better results compared to the Moore one.

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Why is it important?

This study showcases the effectiveness of cellular automaton (CA)-based segmentation techniques on microscopic images by treating images as 2D cellular automata, where pixel values represent cell states, and employing both von Neumann and Moore neighbourhoods for state updates. It reveals that the von Neumann neighbourhood approach outperforms the Moore neighbourhood in segmenting microscopic images, offering a promising direction for image analysis in scientific research.

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