“Medical Image Analysis” offers a contemporary and comprehensive overview of medical image computing and analysis, authored by leading educators and experts in the field. This text serves as a practical and self-contained reference, providing valuable insights into fundamental methodological concepts across various medical domains. Key sections include core representations and properties of digital images, image enhancement techniques, advanced image computing methods (such as segmentation, registration, motion, and shape analysis), and the application of machine learning in medical image computing (MIC).

Key Features:

Fundamental Concepts: Covers core representations and properties of digital images, ensuring a solid understanding of the foundational principles.

Advanced Methods: Explores sophisticated image computing methods, including segmentation, registration, motion analysis, and shape analysis, offering in-depth insights into these techniques.

Machine Learning Applications: Discusses the application of machine learning in MIC, providing a contemporary perspective on the integration of advanced computing methods.

Clinical and Research Use: Illustrates how MIC is employed in clinical settings and medical research, emphasizing practical applications across different medical domains.

Problem-Solving Approach: Guides readers on identifying alternative strategies and using software tools to address common challenges in MIC.

This authoritative text is designed to meet the needs of students, researchers, and practitioners seeking a modern and relevant resource in medical image analysis. With tutorial-based sections that elucidate principles and their real-world applications, the book offers a valuable reference for those navigating the complexities of this evolving field.