医学图像处理属于计算机科学、生物医学工程的交叉学科，在病理分析、疾病的临床诊断、治疗方案的制定等方面有着重要实用价值。本课程主要面向生物医学工程的研究生，且具备基本的数字图像处理知识和扎实的计算机编程能力。 培养目标：培养学生掌握生物医学图像的基本原理，熟悉处理各种不同模态图像的基本方法，并培养学生解决实际问题的能力，培养与社会需求想适应的实用型人才。教学方法：本课程在秋季开设，共54学时。其中授课39学时，课程设计15学时。主要讲授生物医学图像中的基本原理和方法。课程的主要内容包括：生物医学图像处理的基本知识（变分法、可变形模型、图像表示和特征匹配方法等）、图像分割方法（可变形模型、EM方法、Graph-cut方法、基于统计模型的分割方法等）、图像配准方法（线性图像配准、基于偏微分方程的弹性配准方法、基于特征匹配的弹性配准方法）、机器学习方法（聚类和分类方法、统计分析等）。教学内容将结合医学图像处理的最新研究进展进行调整和充实，以保证学生了解和掌握整个学术领域的发展趋势。

Catalog Description: Advanced Biomedical Image Processing (3 credits). Prerequisites: Digital Image Processing. We assume familiarity with the basic knowledge of digital image processing and solid background on statistics and optimization. Proficient programming skill in C/C++ and Matlab is required. Please see us if you are concerned about whether you have the background required for this course.Invitation: Biomedical image processing is the emerging discipline in bioengineering area, which covers a wide range of signal processing, computer vision, and machine learning. The objective of this course is to provide students with an overview of the computational and mathematical methods in biomedical image processing. The images cover the main source of biomedical imaging data (CT, MRI, PET and ultrasound, microscopy, etc.). We will study many of the current state-of-the-art methods used in computational anatomy, computer assisted diagnosis, and surgery navigations. Students interested in medical imaging, as well as health and medicine, will find this course useful. The course is designed for the graduate students with solid background on digital image processing and statistics, who want to learn the conventional and new techniques in biomedical image processing.Course Target:"Have the basic knowledge of biomedical image processing methods in different area, such as image segmentation and registration."Understand the applications related with the biomedical image processing."Independently complete one image processing project."Improve the research skill in paper reading and writing.