ECE 5780 Contents
Note: Lecture Notes and Handouts, Homework and Exams, and Projects and Labs are not available for this course.
Course Description
A variety of powerful imaging modalities with attending computer image processing methods are available for the evaluation of health and the detection of disease. This course will focus on the quantitative analysis of these images and Computer Aided Diagnosis (CAD); that is, the automatic identification and classification of abnormalities by the computer from image data. The confluence of new technology providing more and higher resolution images together with policies for providing low cost non-invasive diagnostic methods is producing an imperative for the development of CAD. Commercial CAD systems for cell analysis and mammography diagnosis are currently available and many more applications are in development. This methodology may also be applied to research in the life sciences where evaluation of biological processes and events may be achieved through observations and analysis of image data. In this context, microscopy is the most frequently used imaging modality. Biomedical image analysis extends conventional computer vision methods in novel directions. Traditional computer vision methods have their foundation in industrial vision applications where the primary modality is the lens based video camera that provides two-dimensional projection images of a three-dimensional scene. However, many biomedical image modalities such as MR, CT, ultrasound, and light microscopy, have the ability to directly acquire true three-dimensional images. Consequently, three-dimensional (and four-dimensional with time) computer vision algorithms will be studied in detail.
Instructor(s)
Anthony P. Reeves
School of Electrical and Computer Engineering
Cornell University
Ithaca, NY 14853
Email: reeves@ece.cornell.edu
Course Level
Undergraduate (senior level)
As Offered In
Spring 2017
Required Text(s)
M. Sonka, V. Hlavac, and R. Boyle “Image Processing, Analysis, and Machine Vision 4th Edition,” Cengage Learning, 2015. The 3rd edition released in 2008 is a suitable alternative.
Course Structure
The course consists of:
- Two weekly lectures
- Lab Exercises and Homework (35%)
- Prelims (30%)
- Final Project (35%)