دانلود کتاب تقسیم بندی تصویر بیومدیکال: پیشرفت و روند
Biomedical Image Segmentation: Advances and Trends, 1ed

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.

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Contents

Chapter 1: Deformable model-based methods for image segmentation
Chapter 2: Domain knowledge for level set segmentation in medical imaging: A review
Chapter 3: Robust image segmentation with a parametric deformable model using learned shape priors
Chapter 4: A 3D active shape model for left ventricle segmentation in MRI
Chapter 5: Model-based segmentation algorithms for myocardial magnetic resonance imaging sequences
Chapter 6: Incorporating shape variability in implicit template deformation for image segmentation
Chapter 7: Exudate detection in fundus images using active contour methods and region-wise classification
Chapter 8: Preprocessing local features and fuzzy logic-based image segmentation
Chapter 9: Model-based curvilinear network extraction toward quantitative microscopy
Chapter 10: Level set-based cell segmentation using convex energy functionals
Chapter 11: Histogram-based level set methods for medical image segmentation
Chapter 12: An appearance-guided deformable model for 4D kidney segmentation using diffusion MRI
Chapter 13: Prostate segmentation using deformable model-based methods: A review
Chapter 14: A novel NMF-based CAD system for early diagnosis of prostate cancer by using 4D diffusion-weighted magnetic resonance images (DW-MRI)
Chapter 15: Distance regularized level sets for segmentation of the left and right ventricles
Chapter 16: Salient object segmentation with a shape-constrained level set
Chapter 17: Tracking and segmentation of the endocardium of the left ventricle in a 2D ultrasound using deep learning architectures and Monte Carlo sampling
Chapter 18: A shortest path approach to interactive medical image segmentation
Chapter 19: Local statistical models for ultrasound image segmentation