Bioimaging and Biomedical data processing - ELABORAZIONE DATI BIOMEDICI (2011/2012)

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    Teaching is organised as follows:
    Activity Credits Period Academic staff Timetable
    Teoria 5 I semestre Andrea Giachetti
    Laboratorio 1 I semestre Andrea Giachetti

    Lesson timetable

    I semestre
    Activity Day Time Type Place Note
    Teoria Wednesday 3:30 PM - 5:30 PM lesson Laboratory Alfa  
    Teoria Friday 10:30 AM - 1:30 PM lesson Lecture Hall H  

    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)