ECE 5780 Contents
Note: Lecture Notes and Handouts, Homework and Exams, and Projects and Labs are not available for this course.
Organization and Content
This course has been offered a number of times and each time there has been a significant difference in the organization. The newness of the topic precludes a set formal outline and each semester new topics are explored through the means of course projects. The following is based on the last offering of this course in the Spring of 2016; some changes may be made this semester depending upon the makeup of the class. A continuing theme for the course is to allow entrance from two groups of students with different backgrounds: (a) engineers and computer scientists with experience in image analysis and (b) biologists, engineers, etc. with experience in the life sciences with or without any image analysis experience.
Prerequisites
This course is open to students with either a biology, computer science, or engineering background. There are no explicit prerequisites for the course; the appropriate background will be discussed at the first class meeting. The course will provide the necessary background on the imaging modalities, the medical issues, and the computer algorithms for image analysis. Having taken either ECE 5470 or the three credit version of BME 6180 would be a very good preparation for this course. This course is intended to meet the needs of both engineers interested in the life sciences and life scientists that are interested in gaining experience in quantitative image analysis methods. Class participation and project research is important. This is a graduate level course and, although it does not have demanding prerequisites, an active interest in biomedical imaging, the maturity to identify and the address any specific knowledge shortcomings, and active class participation are expected.
Lectures
Course lectures fall into three different categories: biomedical image analysis methods, fundamentals of image analysis, and class presentations.
- Biomedical Image analysis methods are the key foundation material for the course and covers image analysis methods, medical image modalities, and statistical methods for evaluating image measurements and CAD tools.
- Image processing fundamentals covers basic image analysis methods and repeats some of the material in ECE 5470. Students who have taken ECE 5470 or an equivalent course are generally excused from these classes.
- Selected application class presentations that are made mainly by the class project groups; some are given by guest lecturers. Each project group will make at least one presentation to the class.
The Class Project
The class project is done in groups of at least three. The project typically consists of background research, and the implementation and evaluation of a specific image analysis method on appropriate image data. Short class presentations of the project proposal and the final project outcome may be required. Each group explores a different problem to provide the broadest experience to the class.
Course Requirements
The grade for this course will be determined from thee components that have similar weight: (a) lab exercises and homework (35%), (b) review exercises (prelims) (30%), and (c) the project (35%). The project will require the submission of a proposal, a final report, and a class presentation may be required. Class presentations are timed and lead to in depth class discussions. There are no absolute thresholds for grades. All work for this course is expected to be original.
Course Objectives
Students having either an engineering or life science background will gain an understanding of the state of the art in the computer analysis of biomedical images. They will have a good knowledge of the image processing algorithms, the essential characteristics of the main image modalities, and the statistical methods to analyze and validate CAD systems. Through the course project students will gain experience in designing and validating computer algorithms for image analysis and in presenting this work to the class audience in a succinct and professional manner.
Academic Integrity
Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student’s own work. For this course collaboration is allowed in the following instance: the class project.