72553 - Biomedical Image Processing (2nd cycle)

Learning outcomes

At the end of the class, the student will have an in-depth expertise in the evaluation and processing of biomedical images from a bioengineering point of view, articulated on mathematical, modeling and computational levels. The student will be able to analyze, develop and implement mathematical models for image processing to be employed in clinical practice applications and to scientific research problems.

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, Bayesian)

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 (Module 2).

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