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Bio-Inspired Algorithms Used in Medical Image Processing
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Author(s): K. Ezhilarasan (Gandhigram Rural Institute, India), K. Somasundaram (Gandhigram Rural Institute, India), T. Kalaiselvi (Gandhigram Rural Institute, India), Praveenkumar Somasundaram (Qualcomm Technologies Inc., USA), S. Karthigai Selvi (Galgotias University, India)and A. Jeevarekha (M.V. Muthiah Government Arts College for Women, India)
Copyright: 2024
Pages: 36
Source title:
Bio-Inspired Optimization Techniques in Blockchain Systems
Source Author(s)/Editor(s): U. Vignesh (Vellore Institute of Technology, Chennai, India), Manikandan M. (Manipal Institute of Technology, India)and Ruchi Doshi (Universidad Azteca, Mexico)
DOI: 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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