Intelligence in medical imaging explores how intelligent computing can create a large amount of changes to existing technology in the field of medical image processing. The book presents various algorithms, techniques, and models for integrating medical image processing with artificial intelligence (AI) and biocomputing. Bioinformatics solutions lead to an effective method for processing the image data for the purpose of retrieving the information of interest and collecting various data sources for extracting the knowledge. Moreover, image processing methods and techniques help scientists and physicians in the medical field with diagnosis and therapies. It describes evolutionary optimization techniques, support vector machines (SVMs), fuzzy logic, a Bayesian probabilistic framework, a reinforcement learning-based multistage image segmentation algorithm, and a machine learning (ML) approach. It discusses how these techniques are used for image classification, image formation, image visualization, image analysis, image management, and image enhancement. The term "medical image processing" illustrates the provision of digital image processing, particularly for medicine. Medical imaging intends to identify internal structures hidden in the human body. It helps to find abnormalities in the body. Digital images can be processed effectively, also evaluated, and utilized in many circumstances concurrently with help of suitable communication protocols.

Chapter Contents:

• 13.1 Introduction
• 13.1.1 What is an image?
• 13.2 Image processing
• 13.2.1 Equivalent image processing
• 13.2.2 Digital image processing
• 13.2.3 Digital image
• 13.2.4 Applications of color models
• 13.2.5 Applications of digital image processing
• 13.2.6 Fundamental steps in digital image processing
• 13.2.7 Components of an image processing system
• 13.3 Medical imaging
• 13.4 Deep learning techniques
• 13.4.1 Uses of image processing
• 13.5 Convolutional neural network
• 13.6 Convolution layers
• 13.6.1 Training phase
• 13.6.2 Training strategies
• 13.6.3 CNN performance
• 13.6.4 Convolutional neutral networks with AI
• 13.6.5 CNN layers
• 13.6.6 CNN image classifier
• 13.7 Deep learning for lung disease detection
• 13.7.1 Preprocessing of images
• 13.7.2 Training
• 13.7.3 Classification
• 13.8 Conclusion
• References

Inspec keywords: support vector machines; data visualisation; image classification; fuzzy logic; image segmentation; reinforcement learning; biocomputing; evolutionary computation; bioinformatics; Bayes methods; image enhancement; medical image processing

Other keywords: reinforcement learning-based multistage image segmentation algorithm; image analysis; digital image processing; biocomputing; term medical image processing; medical field; intelligent computing; image visualization; medical images; image formation; image classification; image management; image processing methods; artificial intelligence; image data; image enhancement; digital images; medical imaging; evolutionary optimization techniques

Subjects: Other topics in statistics; Support vector machines; Graphics techniques; Other topics in statistics; Biocomputing techniques; Optical, image and video signal processing; Biology and medical computing; Biomedical measurement and imaging; Computer vision and image processing techniques