Simulating Bacterial Biofilms -- Distance Transform Algorithms And Their Implementation And Evaluation -- Level Set Techniques For Structural Inversion In Medical Imaging -- Shape And Texture-Based Deformable Models For Facial Image Analysis -- Detection Of The Breast Contour In Mammograms By Using Active Contour Models -- Statistical deformable models for cardiac Segmentation and Functional Analysis In Gated-Spect Studies -- Level Set Formulation For Dual Snake Models -- Accurate Tracking Of Monotonically Advancing Fronts -- Toward Consistently Behaving Deformable Models For Improved Automation In Image Segmentation -- Application Of Deformable Models For The Detection Of Acute Renal Rejection -- Physically And Statistically Based Deformable Models For Medical Image Analysis -- Deformable Organisms For Medical Image Analysis -- Pde-Based Three Dimensional Path Planning For Virtual Endoscopy -- Object Tracking In Image Sequence Combining Hausdorff Distance, Non-Extensive Entropy In Level Set Formulation -- Deformable Model-Based Image Registration.

Deformable Models: Biomedical and Clinical Applications is the first entry in the two-volume set which provides a wide cross-section of the methods and algorithms of variational and Partial-Differential Equations (PDE) methods in biomedical image analysis. The chapters of Deformable Models: Biomedical and Clinical Applications are written by the well-known researchers in this field, and the presentation style goes beyond an intricate abstraction of the theory into real application of the methods and description of the algorithms that were implemented. As such these chapters will serve the main goal of the editors of these two volumes in bringing down to earth the latest in variational and PDE methods in modeling of soft tissues. Overall, the chapters in the first volume provide an elegant cross-section of the theory and application of variational and PDE approaches in medical image analysis. This volume introduces, discusses, and provides solutions for problems ranging from structural inversion to models for cardiac segmentation, and other applications in medical image analysis. Graduate students and researchers at various levels of familiarity with these techniques will find the volume very useful for understanding the theory and algorithmic implementations. In addition, the various case studies provided demonstrate the power of these techniques in clinical applications. Researchers at various levels will find these chapters useful to understand the theory, algorithms, and implementation of many of these approaches.