Biomedical Image Segmentation: Advances and Trends

ISBN-13: 9781482258554 / Angielski / Twarda / 2016 / 526 str.

Ayman El-Baz; Xiaoyi Jiang; Ed. Jasjit S. Suri

Opis

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.

Recenzje

"The book provides a current and thorough overview of some commonly used image segmentation algorithms for medical imaging. The overview of a wide selection of methods is helpful, and I also found it very convenient to see implementation details collected in one single volume. This makes the book a good reference book for both researchers in the field, and could also be helpful for software engineers in the medical industry implementing methods for medical practitioners."
— Kristian Sandberg, Computational Solutions, Inc., Colorado, USA

Spis treści

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.