Digital Image Content and Context Information in Tissue-based Diagnosis
Background and definitions: Image content information (ICI) comprises the information that an external observer can extract solely from the image itself, i.e., without additional notifications (labels, classification, etc.). Image analysis is the procedure to extract meaningful information from the image. It is an important issue in digital pathology and the prerequisite to implement algorithms of diagnosis assistance. Meaningful information (IMI) comprises the information which the observer can understand or which is transformed into an information related reaction. It corresponds to regions of interest (ROI). Pixel and gray value create all information of a digitized image. The spatial distribution of pixel gray values is called texture. Objects are externally defined pixel clusters; their spatial distribution forms a structure.
Living organisms are limited spatially circumscribed systems which are included in external environment and possess an unstable inner volume. Development and limited stability of these open thermodynamic systems are guaranteed by their environment and additional not overlapping inner equivalent systems, which build the so - called hierarchically order of structures. Interaction between objects might alter appearance, development, appearance of new or disappearance of existing objects. The interactions are commonly called functions. They are equivalent to communication and based upon different ‘carriers’ such as electromagnetic signals, macromolecules, salt concentrations, etc.
Detection and description of structures depend upon the observer time. The application of visible macromolecules which bind to boundaries of structures might detect and forecast changes of the boundaries, i.e. structures. The intensity of the visibility and its distribution reflect to the thermodynamic affinity. Detailed analysis of gray value intensities might allow an insight in thermodynamic properties of structures.
Context is defined as information which can be derived from and added to object and structure related information, and, vice versa, might be applied to predefine the observer information capability which is needed to understand the ‘meaning’ of an image, or to select the most capable observer.
Perspectives: The described algorithm is an appropriate tool to combine computerized image content information with its computerized ‘observer’ system. It will be able to measure, acquire, understand and to transfer image content information in an adequate reaction, i.e. diagnosis.
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