93931 - Smart Medical Imaging

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Good health and well-being Partnerships for the goals

Academic Year 2021/2022

Learning outcomes

The course provides students with knowledge concerning the main challenges in medical imaging; students achieve a comprehensive understanding of the use of imaging technologies in a clinical setting and develop the ability to extract clinical information from medical image analysis, to support patient diagnosis and therapy. More specifically, the student: - knows how to choose the most suitable medical imaging acquisition systems and their use in clinics; - is aware of both classic computer vision approaches and innovative and emerging methodologies aimed at biomedical image processing; - knows a large number of methodologies to devise, develop and implement solutions to clinical questions which require medical image processing; - is able to perform critical assessments of the most appropriate processing techniques to solve specific clinical problems; - is able to interpret the results of image processing-based data analysis for applications in clinics; - knows how to interact with both producers and users of medical image processing software, in order to obtain indications for the design and implementation of innovative solutions in the biomedical field.

Course contents

Digital bio-images: acquisition, storage, analysis

Classical methods for image processing (arithmetic, histogram and point operations, equalization, contrast enhancement, increase/reduction of dynamic range, cumulative histogram. Filtering linear/non-linear smoothing/sharpening in the spatial/frequency domain, morphological operators. Object segmentation: fixed and adaptive thresholding, Hough transform, split and merge, region growing, watershed)

Innovative techniques based on evolution equations for filtering and segmentation of bioimages in 2D, 3D and 3D + time (deformable models, statistical shape models)

Classification and clustering techniques (K-means, expectation maximization, mean-shift, KNN, ...)

Machine learning and deep learning for bio-image segmentation and classification

Techniques for bio-images registration (rigid and affine, non-rigid)

Tracking techniques (block matching and optical flow)

Visualization of 3D medical data

Introduction to statistical analysis applied in the bio-image processing field

Seminar lectures will be held on specific topics by experts in the field.

Readings/Bibliography

Handouts and materials provided by the teacher.

Reading the reference scientific articles that will be provided by the teacher on specific topic for an in-depth approach is recommended.

Digital Image Processing Using MATLAB. Gonzalez, Rafael C., Woods, Richard E. Pearson Prentice Hall

Teaching methods

Lectures and exercises in the computer lab.

Each topic will be accompanied with practical examples to highlight significant applications.

Assessment methods

During the course anonymous questionnaires will be administered through web applications to monitor the level of comprehension of the contents in real time and eventually carry out remodulations.

Final oral examination with a grade based on the preparation exhibited by the student on the theoretical part and on exercises carried out during the class. The solution of two assignments is due at least two days before the date of the examination; these solutions will be commented and discussed during the oral test.

Student's ability to illustrate the content covered during the class, also performing comparative evaluations between the different methodologies will be evaluated as well as the capability to solve new problems related to biomedical image processing. Language skills, clarity of presentation, level of detail will be an integral part of the evaluation.

Teaching tools

Notebook, projector, computer lab.

Office hours

See the website of Cristiana Corsi