| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Understand the description of digital images,
- Apply image processing methods to biomedical images,
- Develop algorithms for image processing and analisis,
- Process images using a high level programming language and tool.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | no |
|---|
| Course Description | Medical Images can be processed using analytical and numerical mathematics methods. After processing of medical images, the quality of the images can be improved and the features can be extracted. In this way medical image processing can produce data which assist the specialist about diagnosis and treatment follow up. In this course digitalization, analysis and processing and of medical images are introduced. The contents of the course are digitalization and producing of medical images, filtering in spatial and frequency domain, morphological image processing techniques, image segmentation and feature extraction and event detection from images. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Fundamentals of digital image processing |
|
2nd Week |
Gray level conversion and description |
|
3rd Week |
Fundamentals of digital image filtering |
|
4th Week |
Filtering in spatial domain |
|
5th Week |
Filtering in frequency domain |
|
6th Week |
Special using of digital image filtering techniques |
|
7th Week |
Morphological Image processing |
|
8th Week |
Morphological Image processing |
|
9th Week |
Image segmentation |
|
10th Week |
Wavelets and multi-resolution image processing |
|
11th Week |
Feature extraction from medical images |
|
12th Week |
Event detection from medical images |
|
13th Week |
Event detection from medical images |
|
14th Week |
Project Presentations |
|
15th Week |
Project Presentations |
|
| Recommended or Required Reading | Digital Image Processing, 3rd edition Rafael C. Gonzalez, Richard E. Woods Prentice Hall.
Discrete-time Signal Processing by Oppenheim and R.W. Schafer, Prentice Hall Inc., 1999.
Medical Image Processing Techniques and Applications, Geoffrey Dougherty, Springer, 2011.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Practice
- Presentation
- Report Preparation
- Experiment
- Questions/Answers
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Assignment |
1 |
15 |
| Practice |
1 |
15 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
72 |
72 |
| Preparation for Quiz |
0 |
0 |
0 |
| Individual or group work |
2 |
3 |
6 |
| Preparation for Final exam |
1 |
24 |
24 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
11 |
11 |
| Laboratory (including preparation) |
1 |
24 |
24 |
| Final exam |
1 |
96 |
96 |
| Homework |
1 |
24 |
24 |
| Total Work Load | 299 |
| Total Work Load / 30 (h) | 9.96 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
