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The aim of the work is to increase the visual information in magnetic resonance imaging based susceptibility weighted images. Our approach is to amplify spatial frequency information of the phase mask used to increase susceptibility weighting using a modified version of the human contrast sensitivity function. Thereby, we propose a modified version of the human contrast sensitivity function for use in phase mask creation. Comparison with conventional susceptibility-weighted imaging was undertaken on a qualitative basis and quantitatively with a number of established image quality metrics on ex vivo mouse brain magnetic resonance images obtained at 16.4 T at various echo times. Four experts also compared the quality of in vivo 1.5 and 3 T human brain magnetic resonance images generated with traditional susceptibility weighted imaging and with the new method. We found that parameters of the modified human contrast sensitivity function can be chosen to improve delineation of structural detail of mouse and human brains. Information contained in susceptibility-weighted images generated using the modified human contrast sensitivity function based phase mask corresponds to that in the conventional method, however the visual range over which it is depicted has improved visual perception. Hence, qualitative evaluation of information contained in susceptibility-weighted images can be improved by amplifying spatial frequencies where human contrast sensitivity is reduced., Highlights • Perception of image content is defined by the human contrast sensitivity function. • Human contrast sensitivity function enhanced susceptibility weighted images. • Marked increase in spatial frequency perception of brain images was achieved. • The approach can potentially be used to improve diagnosis of diseases and disorders., Graphical abstract

Formal methods, techniques and tools are, at the present time, an active research topic in different areas of computer science (knowledge representation, real-time systems, algorithms, etc.). These formal techniques are intended to help users specify consistently their needs and verify them. Only mathematical techniques are able to prove or to verify the coherence of the specification of a given system or algorithm, etc. However, there is an enormous difficulty to put into use the mathematical techniques and concepts. This difficulty stems from the fact that these mathematical techniques and concepts are accessible only by a minority of specialists. To solve this problem, we have to develop tools and methods to help users to make the most out of formal approaches, without the apparent complexity of mathematical problems.

This paper is an overview of a three years effort aimed at providing a flexible and powerful model for design applications. The result is a data model called SHOOD based on object-oriented concepts and frame-based knowledge representation. SHOOD implements sophisticated features, such as: object persistence multi-methods along a specific specialization hierarchy, which is independent of the class hierarchy sophisticated semantic relationships e.g, dependency relationships between objects, which are totally independent of the composition relationship multiple object representations, allowing the users to manipulate the objects from several points of views simultaneously the systematic use of a powerful meta-object kernel, which is used to implement a reflexive architecture. This paper is an informal overview of SHOOD. It focuses on the last two issues.

Object-oriented database systems ususally exhibit specific advantages over traditional database management systems and programming languages. Among them stand the ease of writing, maintaining and debugging application programs, code modularity, inheritance, persistency and sharability. Of particular interest to software engineering and computer-aided design applications is also the ability to dynamically change the object definitions and the opportunity to define incrementally composite objects. This paper gives an overview of current research efforts directed towards evolving data definitions in object-oriented database systems. The emphasis is on their ability to support two complementary aspects: supporting evolving schemas, and propagating the changes on the object instances. Several projects are analyzed: Cadb, Encore, GemStone, Orion and Sherpa. Current results indicate that if most of them provide schema evolution facilities, they seldom support automatic propagation mechanisms. A proposal is described that enables Sherpa to fully support the propagation of changes and the dynamic classification of the instances whose class definitions are modified. This approach is an extension of techniques used in artificial intelligence for knowledge representation. It extends previous classification mechanisms with a dynamic capability which adequately supports evolving class definitions and instances.

Current trends in database research emphasize the development of new tools for improved semantics definition and manipulation. It stresses the need for expert knowledge integration in database applications and distributed information usage. Of particular interest today is also the cooperation of artificial intelligence techniques and database technology. This paper presents a new method for database and knowledge base integrity control based on object-oriented concepts and on logic programming. It uses a novel technique for semantic integrity enforcement in database applications, based on the notions of object prototypes and database sample. A prototype is an incomplete object instance created dynamically in the database. It is representative of the family defined by an object declaration in the database schema. A database sample is a set of prototypes. It is used for the certification, i.e the control of the correctness, of all user operations concerning the definition and the modification of the database schema and the data.

This paper gives an overall presentation of the OPALE project. This project aims at designing a database machine oriented toward the execution of PROLOG, considered as data description and data manipulation language.