Beng445a/Eeng445a/Enas912a

Biomedical Image Processing and Analysis

Fall 2010

Resources page: For information on course resources for computer (matlab) assignments.
Engineering Information Technology: For information on computing resources in Engineering (circus, garage).
Yale Classes v2 server: For course information, under "Resources": slides, handouts, problem sets, etc.

Course Number	BENG/EENG 445, ENAS 912
Course Title	Biomedical Image Processing and Analysis
Instructors	James Duncan (james dot duncan at yale dot edu) Lawrence Staib (lawrence dot staib at yale dot edu)
Teaching Assistant	Gary Ho (gary dot ho at yale dot edu) Contact for appointment.
Schedule	TTh 4:00-5:15 at Becton 102

Overview

This course is an introduction to Biomedical Image Processing and Analysis covering image processing basics and techniques for image enhancement, compression, segmentation, registration and motion analysis. Students will learn the fundamentals behind image processing and analysis methods and algorithms with an emphasis on biomedical applications. This course is open to undergraduate and graduate students. We assume students have an understanding of linear systems (Eeng 310 or equivalent) and calculus up to differential equations. In addition, it is also helpful to have a familiarity with elementary probability theory. Please contact the instructors if you have questions regarding your preparation. There will be about ten homeworks and both a midterm and a final exam (during exam period). Homeworks will include Matlab programming assignments. Grading will be based approximately 1/3 on the homeworks, 1/3 on the midterm and 1/3 on the final. Undergraduates and graduates are graded separately; in addition, assignments may differ.

Text:

R. Gonzalez and R. Woods, Digital Image Processing, Prentice Hall (Book Website)

(On reserve in the Engineering Library.)

Chapters 1 and 2 online

Additional readings to be distributed during class.

Course Objectives:

Having successfully taken this course, you will be able to

understand image processing basics including image formation, sampling and quantization
analyze and implement image enhancement techniques
understand, analyze and implement basic techniques in image compression
understand and analyze techniques for the quantification of motion from image sequences measuring dynamic biological phenomena
understand, analyze and implement techniques in segmentation including thresholding, region growing and deformable models
understand and implement the components of gray level and feature-based rigid and nonrigid image registration methods

Course Outline (dates/topics approximate), Fall 2010

		Introduction (Read Gonzalez Ch. 1)
Sep 2	ls	Intro/Organization
		Fundamentals (Read Gonzalez Ch. 2)
Sep 7	ls	Basics / Digitization (to be rescheduled)
		Enhancement (Read Gonzalez Ch. 3)
Sep 9	ls	Gray scale enhancement
Sep 14	ls	Spatial Filtering
Sep 16	ls	Spatial Filtering
Sep 21	ls	Mathematical Morphology (Gonzalez Ch. 9.1-9.3)
		Enhancement (Read Gonzalez Ch. 4)
Sep 23	jd	Enhancement in the frequency domain
Sep 28	jd	Enhancement in the frequency domain
Sep 30	jd	Enhancement in the frequency domain
		Compression (Read Gonzalez Ch. 8)
Oct 5	jd	Compression
Oct 7	jd	Compression
		Rigid and Nonrigid Registration (Read handout)
Oct 12	ls	Registration: Introduction and Transformations
Oct 14	ls	Registration: Match Metrics
Oct 19		Registration: Match Metrics
Oct 21		Midterm Exam
Oct 26	ls	Registration: Optimization and Interpolation
Oct 28	ls	Registration: Robust
		Motion (Read handout)
Nov 2	jd	Motion
Nov 4	jd	Motion
		Segmentation (Read Gonzalez Ch. 10 and handout)
Nov 9	jd	Segmentation
Nov 11	jd	Segmentation
Nov 16	jd	Segmentation
Nov 18	jd	Segmentation
Nov 23		Thanksgiving Break
Nov 25		Thanksgiving Break
Nov 30	ls	Diffusion Weighted Image Analysis
Dec 2		Review

		Reading Period
		Final Exam

Image Processing Links:

Gonzalez and Woods: Digital Image Processing Image Database Image Processing Tutorials
Tutorials on Image Analysis, Computer Vision
Image Processing (wikipedia)
Image Processing Tutorial
A-Z of Image Processing Concepts
efg 's Image Processing Page
One dimensional convolution demonstration: you can draw your own functions or select pre-defined ones.
Two dimensional filtering demonstration: first, Fourier transform, then view the magnitude log; apply gaussian smoothing or other filtering, then inverse transform.
Discrete Fourier Theory (1D)
Matlab Tutorials
Mathworks
Introduction to Image Compression
Image Motion Analysis Bibliography
Deformable Image Segmentation
Image Registration
Homogeneous Coordinates
Image Registration Bibliography
HIPR2: Image Processing Learning Resources with JAVA
Pilot European Image Proc. Archive
The Graphics File Format Page
Watermarking, steganography, information hiding
StirMark:Watermarking Robustness Test
Morphing
Amara's Wavelet Page
Principles of Computerized Tomographic Imaging
Computer Vision Home Page
Computer Vision On-line Bibliography

Sep 2010