Information Package / Course Catalogue
Spectral Imaging
Course Code: MME622
Course Type: Area Elective
Couse Group: Third Cycle (Doctorate Degree)
Education Language: English
Work Placement: N/A
Theory: 3
Prt.: 0
Credit: 3
Lab: 0
ECTS: 8
Objectives of the Course

The aim of the course is to teach principles and applications of advanced digital image processing techniques for spectral image filtering, segmentation, compression, and registration

Course Content

Principles and applications of advanced digital image processing techniques for spectral image filtering, segmentation, compression, and registration

Name of Lecturer(s)
Prof. İsmail BÖĞREKCİ
Learning Outcomes
1.Understanding the role of partial differential equations in image filtering
2.Understanding the role of partial differential equations in image segmentation
3.Understanding advanced techniques and appplications of image registration
4.Understanding digitial image compression standards and techniques
5.Having advanced knowledge and hands on experience on image processing techniques and applications
Recommended or Required Reading
1.Geometric Partial Differential Equations and Image Analysis, YAZAR: Guillermo Sapirodate BASIM: February 2006, Cambridge Press ISBN: 9780521685078
2.Image Processing: Principles and Applications YAZARLAR: Tinku Acharya, Ajoy K. Ray BASIM: October 2005, Wiley ISBN: 978-0-471-71998-4
Weekly Detailed Course Contents
Week 1 - Theoretical
Partial Differential Equations for Filtering (Edge Stopping, Directional, Isotropic and Anisotropic Diffusion Filters)
Week 2 - Theoretical
Model based segmentation (Active Contours)
Week 3 - Theoretical
Explicit (Lagrangian) Geometric Curve and Surface Evaluation: Snakes, Applications and Limitations
Week 4 - Theoretical
Implicit (Eulerian) Geometric Curve and Surface Evaluation: Level Sets, Applications and Limitations
Week 5 - Theoretical
Variational Level Set Methods (Fast Marching)
Week 6 - Theoretical
Geodesic Curves and Minimal Surfaces (Minimal path and centerline extraction techniques)
Week 7 - Theoretical
Statistical Shape Modeling of Image and Volume Data (Shape representation, Shape Model Construction,Appearance models, Shape correspondence, Applications)
Week 8 - Intermediate Exam
Texture extraction (Co-occurence matrices, sum and different histograms, wavelets, curvelets, contourlets,brushlets), Midterm exam
Week 9 - Theoretical
Texture extraction (Co-occurence matrices, sum and different histograms, wavelets, curvelets, contourlets,brushlets)
Week 10 - Theoretical
Image Registration Techniques
Week 11 - Theoretical
mage Compression Techniques (Parameters of image compression, drawbacks of various methods, advantagesof wavelet-based compression techniques, standard and new image formats, strength of new compressiontechniques
Week 12 - Theoretical
Hyper-spectral and Multi-spectral imaging
Week 13 - Theoretical
Multi-dimensional Processing (Multi Planar Reconstruction, Curved and Oblique Sectioning, Volume Rendering,Surface Rendering, Maximum Intensity Projection)
Week 14 - Theoretical
Image Mining and Content Based Image Retrieval
Week 15 - Final Exam
Final exam
Week 16 - Final Exam
Final exam
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory143498
Assignment70535
Individual Work73342
Midterm Examination19211
Final Examination112214
TOTAL WORKLOAD (hours)200
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
PÇ-12
PÇ-13
PÇ-14
OÇ-1
3
4
5
5
4
3
3
4
5
5
4
3
4
5
OÇ-2
3
4
5
5
4
3
3
4
5
5
4
3
4
5
OÇ-3
5
4
3
3
4
5
5
4
3
3
4
5
5
4
OÇ-4
5
4
3
3
4
5
5
4
3
3
4
5
5
4
OÇ-5
3
4
5
5
4
3
3
4
5
5
4
4
3
4
Adnan Menderes University - Information Package / Course Catalogue
2026