Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN412Introduction to Medical Image Analysis3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective of this course is to provide students with an introduction to theory and mathematical methods used in medical image generation and analysis, with a focus on understanding algorithms to solve problems regarding the extraction of information from biomedical image datasets.
Course Content Basic concepts in medical image analysis, 2-D, 3-D, and 4-D biomedical images, volume data, pixels and voxels, file-formats and related practical information, relevant basic mathematical concepts such as registration, segmentation and classification, image acquisition techniques, noise and image enhancement, lossless compression, biomedical image databases, machine learning applications for classification and clustering of images.
Course Methods and Techniques Lecture, discussion, question and answer, preparing and/or presenting reports, problem solving
Prerequisites and co-requisities ( BBM104 ) and ( BBM102 ) and ( AIN414 )
Course Coordinator None
Name of Lecturers Instructor Bölüm Sorumluları
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Rangayyan, R. M. (2004). Biomedical image analysis. CRC press. Dougherty, G. (Ed.). (2011). Medical image processing: techniques and applications. Springer Science & Business Media.
Course Notes Rangayyan, R. M. (2004). Biomedical image analysis. CRC press.

Dougherty, G. (Ed.). (2011). Medical image processing: techniques and applications. Springer Science & Business Media.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 25
Assignment 3 % 30
Attendance 1 % 10
Final examination 1 % 35
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 3 8 24
Preparation for Midterm Exam 1 30 30
General Exam Preparation 1 40 40
Total Work Load   Number of ECTS Credits 5,93333333333333 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completion of this course the students will 1- have a thorough understanding on basic mathematical concepts such as registration, segmentation and classification
2 2- understand the approaches frequently used to solve medical image analysis related problems and be able to apply related algorithms
3 3- understand the principles of current imaging technologies and analysis methods used in medicine such as magnetic resonance imaging, ultrasound, PET and computer tomography.
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Medical images, imaging technologies, data types and structure
2 Digital image processing and image characterization
3 Medical imaging Modalities: MRI, ultrasound, PET, CT
4 Biomedical image Segmentation I: Introduction
5 Biomedical image Segmentation II: statistical classification, morphological operators, connected components
6 Biomedical image Registration I: Rigid and non-rigid transformations, objective functions
7 Midterm exam
8 Biomedical image Registration II: Joint entropy, optimization methods
9 Medical image enhancement and denoising
10 Mathematical morphology & image matching
11 Biomedical image quantification: shape and texture analysis
12 Feature extraction from medical images
13 Pattern recognition on biomedical images and computer aided diagnosis
14 Deep learning based classification and clustering of biomedical images
15 Review and discussion of topics and subjects from previous weeks
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 3 4 4 4 4 4 2 3 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733685&lang=en