An approach to the definition, description, and extraction of structures in binary digital images

Relational tools to manage pictorial information

The final goal of this work is to define a data model to orgamse and manage the indexmg structure of a pictorial mformaeon retrieval system. Relational tools to minage the information drawn trom digital images are presented and discussed Such tools help to organise data according to the numerical/structural user model adopted to devcrihe the im iges Specifically. they allow the storage, mampulation and management of complex pictorial object descriptions which take into account objects, sub-objects and thor composition relationships, as well as the specialisation relationship among their type

Pictorial indexing for an integrated pictorial and textual IR environment

Information retrieval (IR) systems were ongmally devel oped to process, store, search and retrieve narrative informa tion. Structured databases are therefore created to store the items contairung enough information to identify and retneve the onginal documents.

This paper proposes an extension of classification and indexing to pictorial data, specifically to pictorial parts of multimedia documents. This goal is attained by adopting structural techniques for digital image description. The de scriptions of the objects (digital structures) present in the image are proposed as pictorial index terms. Such a generaliza tion has led to a uniform management of the non-homoge neous kinds of data composing the document and has allowed the outlining of a multimedia IR system raised from interdisci plinary experiences.

In the paper the approach to image description is presented and a control structure to index textual and pictorial data is discussed.

Quantitative morphology and shape classification of neurons by computerized image analysis

We describe a new image processing method for semiautomatic quantitative analysis of neuronal morphology. It has been developed in a specific image analysis environment (IBAS 2.0), but the algorithms and the methods can be employed elsewhere. The program is versatile and allows the analysis of histological preparations of different quality on the basis of different levels of evaluation and image extraction. Some significant algorithms have been implemented (i.e. one for multiple focus image acquisition and one for automatic cell body shape recognition and classification). A wide set of specific morphological parameters has been defined to allow a better mathematical characterization of neuronal morphology as regards both dendrite trees and cell bodies. Cell bodies' shapes can be classified automatically, defining different neuronal populations. This is done by evaluating the number of main dendrites and perikarya shapes through a multi-valued-decision-tree based method, tested on somatostatin-positive cells in mouse brain. The methods presented have been applied to analysis of neurons, but they can well be used for any quantitative morphological study of other cell populations.

Automatic quantitative evaluation of autoradiographic band films by computerized image analysis

The present paper describes a new image processing method for automatic quantitative analysis of autoradiographic band films. It was developed in a specific image analysis environment (IBAS 2.0), but the algorithms and methods can be utilized elsewhere. The program is easy to use and presents some particularly useful features for evaluation of autoradiographic band films, such as the choice of whole film or single lane background determination; the possibility of evaluating bands with film scratch artifacts and the quantification in absolute terms or relative to reference values. The method was tested by comparison with laser-scanner densitometric quantifications of the same autoradiograms. The results show the full compatibility of the two methods and demonstrate the reliability and sensitivity of image analysis. The method can be used not only to evaluate autoradiographic band films, but to analyze any type of signal bands on other materials (e.g. electrophoresis gel, chromatographic paper, etc.).