  Nowadays, the amount of heterogeneous biomedical data is increasing more and
more thanks to novel sensing techniques and high-throughput technologies. In
reference to biomedical image analysis, the advances in image acquisition
modalities and high-throughput imaging experiments are creating new challenges.
This huge information ensemble could overwhelm the analytic capabilities needed
by physicians in their daily decision-making tasks as well as by biologists
investigating complex biochemical systems. In particular, quantitative imaging
methods convey scientifically and clinically relevant information in
prediction, prognosis or treatment response assessment, by also considering
radiomics approaches. Therefore, the computational analysis of medical and
biological images plays a key role in radiology and laboratory applications. In
this regard, frameworks based on advanced Machine Learning and Computational
Intelligence can significantly improve traditional Image Processing and Pattern
Recognition approaches. However, conventional Artificial Intelligence
techniques must be tailored to address the unique challenges concerning
biomedical imaging data. This thesis aims at proposing novel and advanced
computer-assisted methods for biomedical image analysis, also as an instrument
in the development of Clinical Decision Support Systems, by always keeping in
mind the clinical feasibility of the developed solutions. In conclusion, the
ultimate goal of these research studies is to gain clinically and biologically
useful insights that can guide differential diagnosis and therapies, leading
towards biomedical data integration for personalized medicine. As a matter of
fact, the proposed computer-assisted bioimage analysis methods can be
beneficial for the definition of imaging biomarkers, as well as for
quantitative medicine and biology.
