Bioimaging and Biomedical data processing (2010/2011)

Course partially running (all years except the first)

Course code
Gloria Menegaz
Teaching is organised as follows:
Unit Credits Academic sector Period Academic staff
BIOIMMAGINI 6 INF/01-INFORMATICS See the unit page See the unit page

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)

Statistics about transparency requirements (Attuazione Art. 2 del D.M. 31/10/2007, n. 544)

Data from AA 2010/2011 are not available yet