As one of the most important tasks in biomedical imaging, image segmentation provides the foundation for quantitative reasoning and diagnostic techniques. A large variety of different imaging techniques, each with its own physical principle and characteristics (e.g., noise modeling), often requires modality-specific algorithmic treatment. In recent years, substantial progress has been made to biomedical image segmentation. Biomedical image segmentation is characterized by several specific factors. This book presents an overview of the advanced segmentation algorithms and their applications.
Brief Surveys of Segmentation Algorithm Classes. Level Set Segmentation: A Survey. Dynamic Programming Based Medical Image Segmentation. Optimal Graph-Based Surface Segmentation and Applications. Medical Image Segmentation Incorporating Physical Noise Models. Atlas-Based Medical Image Segmentation. Applications. Retinal Image Segmentation. Spine Segmentation. Arterial Wall Segmentation. Segmentation of the Left Ventricle. Multi-Atlas-Based Simultaneous Labeling of Longitudinal Dynamic Cortical Surfaces in Infants. Rotational Slice-Based Prostate Segmentation. Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells. Deformable Atlas for Multi-Structure Segmentation. A Variational Framework for Joint Detection and Segmentation of Ovarian Cancer Metastases. Incorporating Shape Variability in Image Segmentation via Implicit Template Deformation. Cell Orientation Entrophy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays. Left Ventricle Segmentation from Cardiac MRI Combining Level Set Methods with Deep Belief Networks. Infrared Target Tracking, Recognition, an Segmentation Using Shape-Award Level Set.