Medical image analysis (2010/2011)

Course code
Name of lecturer
Andrea Giachetti
Andrea Giachetti
Number of ECTS credits allocated
Other available courses
Academic sector
Language of instruction
I semestre dal Oct 4, 2010 al Jan 31, 2011.

Lesson timetable

I semestre
Day Time Type Place Note
Wednesday 12:30 PM - 1:30 PM lesson Lecture Hall F from Nov 2, 2010  to Nov 12, 2010
Wednesday 2:30 PM - 4:30 PM laboratorio Laboratory Alfa from Nov 2, 2010  to Nov 12, 2010
Friday 11:30 AM - 1:30 PM lesson Lecture Hall C from Oct 22, 2010  to Jan 31, 2011

Learning outcomes

To acquire basic knowledge on digital diagnostic images and to understand and learn how to code and apply the most applied algorithms used for image/volume visualization, segmentation, registration and classification.


1. Diagnostic imaging.

Goal: a review of image processing and an overview on images in hospitals.
-Digital images and related processing.
-Diagnostic imaging modalities: CT, MRI, US, PET, ecc.
-DICOM: image communication and archive in medicine

2. Visualization in radiology
-Overview of medical image applications: Computer Aided Diagnosis, surgical planning, simulation
- Volume data visualization, Surface and Volume rendering techniques

3. 3D data segmentation and visualization.
Goal: Describing the most used 3D-4D recosntruction and visualization used in the medical practice
-Thresholding, region growing, mathematical morphology
-Methods based on clustering in color space, Graph cuts, Watershed, MRFs
-"Snakes" and other 2D/3D deformable models
- Model based approaches

4. Image registration.
Goal: Introducing methods and applications of 2D/3D image registration
- Image based registration: rigid/nonrigid transforms, difference measures, interpolation methods, optimization approaches
- Point based registration: ICP, robust methods, related problems

5. Motion analysis

Goal: Introducing the computer vision techniques used to recover motion from image sequences.
- Motion field and optical flow
- Optical flow algorithms: block matching, Lucas-Kanade

6. Shape analysis
- Region/volume processing, feature extraction, distance functions, curve skeletons

7. Texture analysis

Goal: Introducing texture analysis and methods to extract features and characterize tissues appearance in diagnostic images
-Texture analysis basics
-Texture features: Gray Level Co-Occurrence Matrices. Run Length Matrices, Wavelets
-Supervised classification

Assessment methods and criteria

Written exam (20/30) and evaluation of a small project (10/30)