• Decision Support
• Image Mining
• Medical Imaging
• Visual Content Representation

Mining image content means to extract image hidden patterns, identify
image data relationships and, thus, gather novel meaningful knowledge
pertinent to the specific domain images belong to. Research in the field
is still in its early stages, although it relies on rather assessed disciplines
such as Computer Vision, Image Processing, Image Retrieval, Data
Mining, Machine Learning, and Artificial Intelligence.
The key importance that nowadays characterizes imagebased
tasks, i.e., tasks that relies on the management, analysis and
interpretation of image content, is plainly perceivable in almost all the
strategic social, scientific and industrial fields: an imaging investigation
is a fundamental step of the medical diagnosis processes; in situ images
are acquired for industrial inspection; biometric images are used in
surveillance or forensic sciences; georeferenced imagery are gathered
and employed in fields such as aerospace, defence, geophysics,
intelligence, oceanography, and so forth. Furthermore, advances in
image acquisition and management technologies have fuelled the rapid
growth of large and rich image collections. These can reveal meaningful
information if suitably processed and exploited. Research in mining
image content is just devoted to reach this goal.
Image Mining can be seen as the summa and advancement of
several processing procedures that are usually applied in image
analysis. It requires a long chain that starts with image acquisition and
storage, evolves through image processing, image content extraction
and suitable representation, image retrieval and indexing, and ends up
with the identification of meaningful patterns, thus allowing the
production of novel knowledge relevant to the task to be solved. The
fundamental challenge in Image Mining is to determine how low‐level
information contained in a raw image or image sequence can be
processed to identify high‐level information, and relationships among
imagery data, as well as with other contextual data.
This dissertation reports the investigation that was carried out in
the field of Image Mining by facing several issues related to the different
steps of the chain. Theoretical investigations were grounded into the
development of innovative methods for tackling all the phases of the
image mining process, from image content extraction, representation
and browsing, to data mining for the generation of novel knowledge.
Such methods were finally integrated into a framework able to support
the main image mining functionalities, ranging from image storage to
novel knowledge discovery. In accordance with the great value inherent
in clinical images, and the increasing amount of digital images available
in medical research, medical imaging was selected among the eligible
application domains, and some case studies belonging to cardiology and
microscopy were considered.