Medical Image Analysis

Introduction: medical image analysis in clinical practice.

Image-guided medical procedures: intrinsic and extrinsic information-based tracking and navigation, procedure planning and visualization, registration of pre- and intra-interventional imaging data, patient models and treatment plans, validation of registration methods, applications of image-guided procedures.

Segmentation and quantitative analysis: classification and applicability of methods, thresholding, edge- and region-based techniques, model- and atlas-based methods, supervised and unsupervised methods, cluster-based, principal component analysis, statistical shape and appearance models.

Computer-aided diagnosis: feature selection and extraction, decision functions, distance measures in cluster analysis, statistical classification, fuzzy classification, neural networks, receiver operating characteristics (ROC), demonstration of successful applications.

The students will obtain a theoretical and practical insight into the computational and mathematical methods in medical image processing and analysis. Student will be able to design  automated methods for image enhancement and registration, segmentation and quantification of medical images, such as X-ray, CT, MRI, PET, etc., in the context of automatic image-based diagnosis and monitoring, and image-guided medical interventions.  They will have the capacity to design, acquire and organize the imaging data and reference data and measurements and use this data to objectively and critically assess the quality of image analysis methods.

Theoretical foundations of automated methods and exemplar use cases are given during lectures, while practical skills are gained through lab works, weekly labwork assignements and individual seminar work.