The aim of this course is to introduce some image processing and visualization techniques not studied in the other courses and mainly applied to the medical field.
In detail, modalities to acquire digital diagnostic images will be analyzed as well as protocol to store and transmit the data.
Algorithms to process 2D and 3D images will be then described and practical applications will be performed using Matlab.
1. Digital images and 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
-Overview of medical image applications: Computer Aided Diagnosis, surgical planning, simulation
2. 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
- "Snakes" and other 2D/3D deformable models
- Extracting curve skeletons
- Surface and Volume rendering
3. Texture analysis
Goal: Introducing texture analysis and methods to extract features and characterize tissues
-Texture analysis
-Texture features: Gray Level Co-Occurrence Matrices. Run Length Matrices, Wavelets
-Supervised classification
4. Motion analysis
Obiettivo: Introducing the computer vision techniques used to recover motion from image sequences.
- Motion field and optical flow
- Optical flow algorithms: block matching, Lucas-Kanade
5. Image registration.
Obiettivo: Introducing methods for the 2D/3D
- Rigid registration.
- Non rigid registration, Iterative methods
Written test. Possible integration with lab activity.
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