Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| MEDICAL IMAGE ANALYSIS | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Cihan Bilge KAYASANDIK |
| Name of Lecturer(s) | Assist.Prof. Cihan Bilge KAYASANDIK |
| Assistant(s) | |
| Aim | The course aims to show how to identify the problems in medical sciences and how to approach them. We will introduce mathematical techniques to extract information from medical images. We will show how to analyze medical images according to different purposes and help diagnose diseases. Medical image analysis is a highly interdisciplinary field involving medicine, computer science, mathematics, biology, statistics, probability, psychology, and other fields. The course includes topics in medical image acquisitions: basics of Xray CT, Ultrasound, MRI and fMRI; image preprocessing: image denoising, image filtering, and basic filter design, image enhancement, feature extraction; image segmentation: local and adaptive thresholding, active contour and level set methods, edge detection, basic texture analysis; image registration, tracking; machine learning and deep learning for the feature extraction and segmentation purposes in medical images. This course will be application-oriented. Assignments will be based on a literature review, paper presentation, and computer implementations. |
| Course Content | This course contains; ,,,,,,,,,,,. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| identify problems in the medical image analysis and form a framework to solve the problem. | 10, 12, 13, 16, 18, 19, 2, 21, 3, 37, 4, 6, 9 | D, F |
| determine the correct filter for extracting certain features from the images and ability to apply them. | 10, 12, 13, 16, 18, 19, 21, 3, 37, 4, 6, 9 | D, F |
| use of mathematical tools to design tools for certain purposes of medical data analysis | 10, 13, 14, 16, 18, 19, 21, 3, 37, 4, 6, 9 | D, F |
| use machine learning and deep learning methods for classification of medical data. | 10, 12, 13, 16, 18, 19, 2, 21, 3, 37, 9 | D, F |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 3: Problem Baded Learning Model, 37: Computer-Internet Supported Instruction, 4: Inquiry-Based Learning, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | D: Oral Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | ||
| 2 | ||
| 3 | ||
| 4 | ||
| 5 | ||
| 6 | ||
| 7 | ||
| 8 | ||
| 9 | ||
| 10 | ||
| 11 | ||
| 12 |
| Resources |
| Lecture notes will be supplied by instructor but following textbooks could be used as supplementary materials. 1. Fundamentals of Medical Textbook Imaging, Suetens, P., Cambridge University Press, 2. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Yoo, Terry S., CRC Press |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | ||||||
| 5 | Independently conduct studies that require proficiency in the field. | X | |||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | ||||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | X | |||||
| 9 | Define the social and environmental aspects of engineering applications. | ||||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 1 | 3 | 3 | |||
| Guided Problem Solving | 0 | 0 | 0 | |||
| Resolution of Homework Problems and Submission as a Report | 5 | 1 | 5 | |||
| Term Project | 2 | 2 | 4 | |||
| Presentation of Project / Seminar | 2 | 3 | 6 | |||
| Quiz | 1 | 3 | 3 | |||
| Midterm Exam | 1 | 4 | 4 | |||
| General Exam | 1 | 5 | 5 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 30 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(30/30) | 1 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| MEDICAL IMAGE ANALYSIS | - | Spring Semester | 3+0 | 3 | 6 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | Second Cycle (Master's Degree) |
| Course Type | Elective |
| Course Coordinator | Assist.Prof. Cihan Bilge KAYASANDIK |
| Name of Lecturer(s) | Assist.Prof. Cihan Bilge KAYASANDIK |
| Assistant(s) | |
| Aim | The course aims to show how to identify the problems in medical sciences and how to approach them. We will introduce mathematical techniques to extract information from medical images. We will show how to analyze medical images according to different purposes and help diagnose diseases. Medical image analysis is a highly interdisciplinary field involving medicine, computer science, mathematics, biology, statistics, probability, psychology, and other fields. The course includes topics in medical image acquisitions: basics of Xray CT, Ultrasound, MRI and fMRI; image preprocessing: image denoising, image filtering, and basic filter design, image enhancement, feature extraction; image segmentation: local and adaptive thresholding, active contour and level set methods, edge detection, basic texture analysis; image registration, tracking; machine learning and deep learning for the feature extraction and segmentation purposes in medical images. This course will be application-oriented. Assignments will be based on a literature review, paper presentation, and computer implementations. |
| Course Content | This course contains; ,,,,,,,,,,,. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| identify problems in the medical image analysis and form a framework to solve the problem. | 10, 12, 13, 16, 18, 19, 2, 21, 3, 37, 4, 6, 9 | D, F |
| determine the correct filter for extracting certain features from the images and ability to apply them. | 10, 12, 13, 16, 18, 19, 21, 3, 37, 4, 6, 9 | D, F |
| use of mathematical tools to design tools for certain purposes of medical data analysis | 10, 13, 14, 16, 18, 19, 21, 3, 37, 4, 6, 9 | D, F |
| use machine learning and deep learning methods for classification of medical data. | 10, 12, 13, 16, 18, 19, 2, 21, 3, 37, 9 | D, F |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 14: Self Study Method, 16: Question - Answer Technique, 18: Micro Teaching Technique, 19: Brainstorming Technique, 2: Project Based Learning Model, 21: Simulation Technique, 3: Problem Baded Learning Model, 37: Computer-Internet Supported Instruction, 4: Inquiry-Based Learning, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | D: Oral Exam, F: Project Task |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | ||
| 2 | ||
| 3 | ||
| 4 | ||
| 5 | ||
| 6 | ||
| 7 | ||
| 8 | ||
| 9 | ||
| 10 | ||
| 11 | ||
| 12 |
| Resources |
| Lecture notes will be supplied by instructor but following textbooks could be used as supplementary materials. 1. Fundamentals of Medical Textbook Imaging, Suetens, P., Cambridge University Press, 2. Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Yoo, Terry S., CRC Press |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Develop and deepen knowledge in the same or in a different field to the proficiency level based on Bachelor level qualifications. | X | |||||
| 2 | Conceive the interdisciplinary interaction which the field is related with. | X | |||||
| 3 | Use of theoretical and practical knowledge within the field at a proficiency level and solve the problem faced related to the field by using research methods. | X | |||||
| 4 | Interpret the knowledge about the field by integrating the information gathered from different disciplines and formulate new knowledge. | ||||||
| 5 | Independently conduct studies that require proficiency in the field. | X | |||||
| 6 | Take responsibility and develop new strategic solutions as a team member in order to solve unexpected complex problems faced within the applications in the field. | ||||||
| 7 | Evaluate knowledge and skills acquired at proficiency level in the field with a critical approach and direct the learning. | X | |||||
| 8 | Investigate, improve social connections and their conducting norms with a critical view and act to change them when necessary. Communicate with peers by using a foreign language at least at a level of European Language Portfolio B2 General Level. | X | |||||
| 9 | Define the social and environmental aspects of engineering applications. | ||||||
| 10 | Audit the data gathering, interpretation, implementation and announcement stages by taking into consideration the cultural, scientific, and ethic values and teach these values. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 50 | |
| Rate of Final Exam to Success | 50 | |
| Total | 100 | |