Computational Intelligence Techniques for Pattern Recognition in Biomedical Image Processing Applications

Computational Intelligence Techniques for Pattern Recognition in Biomedical Image Processing Applications

D. Jude Hemanth (Karunya University, India) and J. Anitha (Karunya University, India)
DOI: 10.4018/978-1-4666-1833-6.ch012
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Abstract

Medical image classification is one of the most widely used methodologies in the biomedical field for abnormality detection in the anatomy of the human body. Image classification belongs to the broad category of pattern recognition in which different abnormal images are grouped into different categories based on the nature of the pathologies. Nowadays, these techniques are automated and high accuracy combined with low convergence rate has become the desired features of automated techniques. Artificial Intelligence (AI) techniques are the highly preferred automated techniques because of superior performance measures. In this chapter, the application of AI techniques for pattern recognition is explored in the context of abnormal Magnetic Resonance (MR) brain image classification. This chapter illustrates the theory behind the AI techniques and their effectiveness for practical application in medical image classification. Few experimental results are also provided to aid the conclusions. Algorithmic approach of the AI techniques such as neural networks, fuzzy theory, and genetic algorithm are also dealt in this chapter.
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Background

Modern medical imaging technology such as MRI has given physicians a non-invasive means to visualize internal anatomical structures and diagnose a variety of diseases. MR images are typically interpreted visually and quantitatively by radiologists. The need for quantitative information is becoming increasingly important in clinical and surgical environment. Brain tumors are the leading cause of cancer death among humans. Hence early detection and correct treatment based on accurate diagnosis are important steps to avoid any fatal results.

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