Contents: Level Set Methods in Medical Imaging: Approaches and Applications.- Breast Boundary Estimation Using Active Deformable Models.- Application of Deformable Models in Ultrasound Breast Imaging.- Inertial snake for contour detection in Ultrasonography images.- MGRF Controlled Deformable Models with Shape Constraints: Applications to Quantify Kidney Function after Implants.- Deformable Models and Level Set for Medical Imagery.- Level Set for Medical Imagery.- Deformable Models for 3-D Ultrasound Prostate Imaging.- Application of 3-D Deformable Models for Bioinformatics Imaging.- PDE and Level set approaches in 4D for analysis of brain surface in dyslexia.- Deformable Models for CT.- Deformable Models in Orthopedics.- Level Set in Electrical Impedance Tomography.- Deformable Models used in Pathology Imaging.- Future of Deformable Models: Critical Issues.

This book covers the complete spectrum of deformable models, its evolution as an imagery field and its use in many biomedical engineering and clinical application disciplines. The book focuses on the core image processing techniques, theory and biomaterials useful to research and industry. Contributors are all pioneers in the field.

This book covers the complete spectrum of deformable models, its evolution as an imagery field and its use in many biomedical engineering and clinical application disciplines. It includes level sets, PDEs, curve and surface evolution and their applications in biomedical fields covering both static and motion imagery.

Introduction to Probability Theory with Engineering Applications provides students with a solid foundation in probability theory, which deals with the modeling of uncertainty, and illuminates several modern applications of probability in engineering, physics and data analysis. The text is organized into five chapters and three appendices. The opening chapter introduces the notion of probability as a model or representation for the uncertainty associated with statistical experiments. In additional chapters, students learn about random variables through explanations of discrete and continuous variables, conditional distribution, and statistical distribution. Students examine functions of one random variable, two random variables, and extensions to multivariable distributions. The final chapter covers random processes. Helpful appendices include six computer laboratories that correspond with the content in Chapters 2-5, assessment and review questions for each chapter, and basic results from linear algebra. The book is an ideal resource for courses in engineering, computer science, biomedicine, physics, and mathematics. It is also an excellent text for researchers seeking an overview in applied probability theory. It is assumed readers have a background in introductory calculus and computer programming.