Intelligent Computing Techniques in Biomedical Imaging

Provides comprehensive and state-of-the-art applications of Computational Intelligence techniques used in biomedical image analysis for disease detection and diagnosis. The book offers readers a stepwise approach from fundamental to advanced techniques using real-life medical examples and tutorials. The editors have divided the book into five sections, from prerequisites to case studies.

Section I: Prerequisites

Presents the prerequisites, where the reader will find fundamental concepts needed for advanced topics covered later in this book. This primarily includes a thorough introduction to Artificial Intelligence, probability theory, and statistical learning.

Section II: Computational Intelligence Methods

Covers methods for medical image acquisition and pre-processing for biomedical images. Readers will find AI applied to conventional and advanced biomedical imaging modalities such as X-rays, CT scan, MRI, Mammography, Ultrasound, MR Spectroscopy, Positron Emission Tomography (PET), Ultrasound Elastography, Optical Coherence Tomography (OCT), Functional MRI, Hybrid Modalities, as well as pre-processing topics such as medical image enhancement, segmentation, and compression.

Section III: Description and Representation of Medical Images

Discusses various categories of features and their relevance in different medical imaging tasks. This section also covers feature selection techniques based on filter method, wrapper method, embedded method, and more.

Section IV: Classification Techniques

Covers Computational Intelligence techniques used for medical image classification, including Artificial Neural Networks, Support Vector Machines, Decision Trees, Nearest Neighbor Classifiers, Random Forest, clustering, extreme learning, Convolution Neural Networks (CNN), and Recurrent Neural Networks. It also discusses computer-aided diagnosis and performance evaluation in radiology.

Section V: Case Studies

Provides a wealth of real-world case studies for Computational Intelligence techniques in applications such as neuro-developmental disorders, brain tumor detection, breast cancer detection, bone fracture detection, pulmonary imaging, thyroid disorders, imaging technologies in dentistry, diagnosis of ocular diseases, cardiovascular imaging, and multimodal imaging.

- Introduces Fourier theory and signal analysis tailored to applications in optical communications devices and systems

- Provides strong theoretical background, making it a ready resource for researchers and advanced students in optical communication and optical signal processing

- Starts from basic theory and then develops descriptions of useful applications