Riemannian Geometric Statistics In Medical Image Analysis

Explore the application of Riemannian geometry to medical image analysis in “Riemannian Geometric Statistics In Medical Image Analysis.” This comprehensive guide delves into the mathematical frameworks and computational techniques for analyzing medical images using Riemannian manifolds. Discover how to represent image data as points on curved spaces, enabling powerful statistical analysis and improved diagnostic accuracy. The book covers fundamental concepts, including Riemannian metrics, geodesics, tangent spaces, and parallel transport. Learn how to apply these concepts to various medical imaging modalities, such as MRI, CT, and diffusion tensor imaging. Explore advanced topics like shape analysis, image registration, segmentation, and classification. Benefit from real-world examples and case studies that illustrate the practical application of Riemannian geometric statistics in clinical settings. Gain insights into the latest research and future directions in this rapidly evolving field. This book is an invaluable resource for researchers, students, and practitioners in medical image analysis, computer vision, and related disciplines. Dive into the world of curved spaces and unlock new possibilities for extracting meaningful information from medical images.