Biomedical Imaging Analysis

Code:

EBE0056

Acronym:

AIBI

Keywords
Classification	Keyword
OFICIAL	Biomedical Engineering

Instance: 2011/2012 - 2S

Active?	Yes
Web Page:	http://moodle.fe.up.pt
Responsible unit:	Department of Electrical and Computer Engineering
Course/CS Responsible:	Master in Bioengineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MEB	14	Syllabus	1	-	6	56	162
MIB	31	Syllabus	3	-	6	56	162
MIEEC	13	Syllabus (Transition) since 2010/2011	4	-	6	56	162
			5
		Syllabus	4	-	6	56	162
			5
PRODEB	0	Syllabus	1	-	6	56	162

Teaching language

Portuguese

Objectives

This course unit aims to develop students’ skills and knowledge in: concepts and methodologies of digital image processing; principles, concepts, physics methods and technologies of imaging used in Biology and Medicine; presentation of the various processes of Image Processing and Analysis in Biology and Medicine (IPA- BM)

Learning Outcomes:
- acquisition of knowledge in IPA- BM
- analysis of problems in IPA- BM
- design of IPA- BM
- oral and written presentation

Program

1. INTRODUCTION
1.1. The image processing cycle
1.2. The machine and computer vision cycle
1.3. The Biomedical image analysis cycle
1.4. Applications
2. DIGITAL IMAGES: ACQUISITION, SAMPLING, QUANTIZATION AND REPRESENTATION
2.1. Introduction
2.2. Digital Images
2.2.1. Image acquisition
2.2.2. Sampling and quantization
2.2.3. An image in the frequency domain
2.2.4. Type of images
2.3. Biomedical Images
2.3.1. Microscopic Images
2.3.2. Light intensity images
2.3.3. Ultrasonic images
2.3.4. X ray images
2.3.5. CT and MR images
2.3.6. PET and SPECT
2.3.7. Thermographic images
3. IMAGE ENHANCEMENT
3.1. Basic intensity operations
3.1.1. Pixel operations
3.1.2. Image averaging
3.1.3. Image subtraction
3.1.4. Intensity histograms
3.2. Image enhancement using local operators
3.2.1. Smoothing filters
3.2.2. Sharpening filters
3.2.3. Edge enhancement
3.2.4. Non-linear filters
3.3. Adaptive image filtering
3.3.1. Wiener Filters
3.3.2. Anisotropic filtering
4. EDGE and CORNER DETECTION
4.1. Introduction
4.1.1. Initial considerations
4.1.2. Goals of edge and corner detection
4.1.3. Types of edges and corners
4.1.4. Basic definitions
4.2. Edge detection
4.2.1. First and second order derivative based operators
4.2.2. Canny Edge detector
4.2.3. Criteria for evaluating the performance of edge detectors
4.3. Line and curve fitting
4.3.1. Edge linking
4.3.2. Hough transform
4.4. Corner detectors
4.4.1. Introduction
4.4.2. Harris detector
5. MORPHOLOGICAL IMAGE PROCESSING
5.1. Basic principles
5.2. Binary images
5.2.1. Erosion and Dilation
5.2.2. Opening and Closing
5.2.3. Thinning
5.3. Grey level images
5.3.1. Basic operations
5.3.2. Morphological smoothing and gradient
5.3.3. Top-hat transform
5.4. Applications
6. IMAGE SEGMENTATION
6.1. Introduction
6.1.1. From images to objects
6.1.2. Goals, definition and overview
6.1.3. Categorization of segmentation methods
6.2. Feature domain
6.2.1. Brightness and colour
6.2.2. Texture
6.2.3. Clustering in the feature domain
6.3. Image domain
6.3.1. Region-based
6.3.2. Boundary-based
7. QUANTITATIVE IMAGE ANALYSIS
7.1. Introduction
7.1.1. Discrete geometry
7.1.2. Connected components labelling
7.2. Feature measurement
7.2.1. Boundary measures
7.2.2. Region measures
7.2.2.4. Invariant Moments
7.3. Object representation
7.3.1. Boundary
7.3.2. Region representation
7.3.2.3. Skeletons
8. IMAGE REGISTRATION AND MOSAICING
8.1. Fundamentals of image registration
8.1.1. Feature selection
8.1.2. Feature correspondence
8.1.3. Transformation functions
8.1.4. Resampling
8.2. Image mosaising
8.2.1. Determination of the global transformation
8.2.2. Image blending
9. MEDICAL IMAGING APPLICATIONS
9.1. In Ophthalmology
9.2. In Thoracic Imaging
9.3. In Chromatographic Imaging
9.4. Ultrasonic Imaging

Mandatory literature

Gonzalez, Rafael C; Digital image processing using Matlab. ISBN: 0-13-008519-7
Gonzalez, Rafael C; Digital image processing. ISBN: 0-201-50803-6

Teaching methods and learning activities

Theoretical-practical classes are theoretical and they will be based on exercises and on the development of programs of biomedical image processing and analysis, as well as the development of a project.
In order to develop students’ oral presentation skills and analysis of results, they have to make a presentation of about 20 minutes on a given theme, which will be chosen two weeks before.

Software

Matlab 6 R12.1

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	-4,00
	Total:	-	0,00

Eligibility for exams

To be admitted to exams, students:
1- cannot miss more theoretical-practical classes than allowed by the rules
2- have to reach a minimum grade of 50% in the continuous assessment component

Calculation formula of final grade

Continuous assessment with final exam
1. Tests (70%)
1st test: April
2nd test: at the day of the final exam

2. Continuous Assessment (30%)
Group assignment (three students)

Recurso (resit) exam

Examinations or Special Assignments

Tests: two tests and an exam
Special Assignments:
Individual presentation of topics related to the program in theoretical-practical classes (20 minutes), professor information (20%)
Group assignment (two students) (20%)

Special assessment (TE, DA, ...)

Students with a special status will be assessed as regular students.

Classification improvement

Students have to attend recurso (resit) exam to improve their grades. The exam will cover the entire program.