Course Description
Gain in-depth understanding, knowledge, and the ability to apply cutting-edge methods to process and quantitatively analyze images. This class presents modern image analysis tools, including wavelets, adaptive filters, active contours, and fractals. An important aspect is the design of unsupervised image analysis chains.
Athena Title
Advanced Image Analysis
Prerequisite
CSEE 4630 or ENGR 4540/6540 or ELEE 4540/6540 or CSCI 8810 or CSCI 8820 or permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Course Objectives
By the end of this course, students should have: – strengthened their prerequisite knowledge of fundamental image processing operations – gained the ability to design a process to extract specific information from an image – gained an understanding of advanced computer algorithms that are used to enhance, analyze, quantify, or visualize images – understood the implications of processes that require supervision, partial supervision, or that work unsupervised – gained the ability to implement specific algorithms using a suitable programming language – applied their knowledge on medical or non-medical sample images in lab-based projects and in teamwork – understood current trends of medical imaging by being exposed to topical literature – advanced their communication skills through presentation of a lecture and presentation of the semester project
Topical Outline
Block 1: Survey of fundamental image processing steps: Enhancement, segmentation, quantification. Block 2: The design of an image processing chain. Supervised, user-aided, and unsupervised operations. Blocks 3 and 4: The Fourier transform, discrete cosine transform, and Hartley transform. Frequency-domain filtering. Wiener filtering. Block 5: Adaptive filters in the spatial and frequency domain. Blocks 6 and 7: The wavelet transform and wavelet-based filtering. Block 8: Active contours and deformable models. Block 9: Segmentation and object recognition with the Hough transform. Block 10: Clustering techniques and multispectral image segmentation. Block 11: Texture classification and quantification. Block 12: Shape extraction, shape quantification and classification. Block 13: Fractal approaches to image quantification. Block 14: 2D and 3D image visualization. Block 15: Trends and current developments in image processing. Additional aspects of the class: Semester Project: Students will apply a combination of design and implementation skills to solve a large semester project. Teamwork: The semester project is to be done in a team if possible. Lecture: Students would be required to give one full-length class lecture of a topic of their choice.