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Bio-Inspired Algorithms Used in Medical Image Processing

Bio-Inspired Algorithms Used in Medical Image Processing

K. Ezhilarasan, K. Somasundaram, T. Kalaiselvi, Praveenkumar Somasundaram, S. Karthigai Selvi, A. Jeevarekha
Copyright: © 2024 |Pages: 36
ISBN13: 9798369311318|ISBN13 Softcover: 9798369345115|EISBN13: 9798369311325
DOI: 10.4018/979-8-3693-1131-8.ch002
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MLA

Ezhilarasan, K., et al. "Bio-Inspired Algorithms Used in Medical Image Processing." Bio-Inspired Optimization Techniques in Blockchain Systems, edited by U. Vignesh, et al., IGI Global, 2024, pp. 25-60. https://doi.org/10.4018/979-8-3693-1131-8.ch002

APA

Ezhilarasan, K., Somasundaram, K., Kalaiselvi, T., Somasundaram, P., Karthigai Selvi, S., & Jeevarekha, A. (2024). Bio-Inspired Algorithms Used in Medical Image Processing. In U. Vignesh, M. M., & R. Doshi (Eds.), Bio-Inspired Optimization Techniques in Blockchain Systems (pp. 25-60). IGI Global. https://doi.org/10.4018/979-8-3693-1131-8.ch002

Chicago

Ezhilarasan, K., et al. "Bio-Inspired Algorithms Used in Medical Image Processing." In Bio-Inspired Optimization Techniques in Blockchain Systems, edited by U. Vignesh, Manikandan M., and Ruchi Doshi, 25-60. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1131-8.ch002

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Abstract

Medical image processing plays a crucial role in diagnosing diseases, guiding treatment plans, and monitoring patient progress. With the increasing complexity and volume of medical imaging data, there is a growing need for advanced techniques to extract meaningful information from these images. Traditional methods in medical image processing often face challenges related to image enhancement, segmentation, and feature extraction. These challenges stem from the inherent variability, noise, and complexity of medical images, making it difficult to obtain accurate and reliable results. In this chapter, the focus is on leveraging bio-inspired algorithms to address these challenges and improve the analysis and interpretation of medical images. Bio-inspired algorithms draw inspiration from natural processes, such as evolution, swarm behavior, neural networks, and genetic programming. It addresses the challenges and requirements specific to each modality and how bio-inspired algorithms can be adapted and tailored to meet those needs.

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