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Biomedical subjects

C Le Bozec

Publications and source records attributed to C Le Bozec.

6 recordsLinked to original sources
PubMed2000

A property concept frame representation for flexible image-content retrieval in histopathology databases.

In histopathology databases, images descriptions are collections of properties provided by experts. Image content retrieval implies comparison of such properties. The objective of this work is to enrich the traditional attribute-value representation of properties in order to take into account the polymorphism and subjectivity of properties and to manage the comparison process. In this paper we define a property concept frame (PCF) representation based on fuzzy logic to handle both representation and comparison. Seven quantifiable morphological characteristics were selected from histopathological reports to illustrate the variety of fuzzy predicates and linguistic terms in properties. The PCF representation has been tested in the context of breast pathology. It is concluded that the PCF representation provides a unification scheme to retrieve in images morphological characteristics that are described in different ways. It may enhance the relevancy of applications in various contexts such as image content-based retrieval or case-based reasoning from images.

Breast↗
PubMed2000

Towards content-based image retrieval in a HIS-integrated PACS.

Development of a Picture Archiving and Communications System (PACS) with a large and diverse set of medical images will lead to large digital libraries that can be accessed to provide improved support for patient care, research and education. New representational and retrieval models for clinical images are required to address these issues. The PACS at the Georges Pompidou Hospital (GPH) is integrated in the hospital information system (HIS), and several modalities from medical imaging departments have been attached to it. The two main axes of the GPH PACS project were 1) HIS-integration to allow hospital-wide access to the images based on demographic and procedure-type information and 2) the development of content-based image retrieval to enhance the medical impact of image retrieval in daily practice.

Breast↗
PubMed1999

A visual coding system in histopathology and its consensual acquisition.

Divergent descriptions of histopathologic images induce inter- and intra-observer variability in diagnosis. Even though a controlled terminology exists to describe medical imaging, pathologists do not always agree on the visual representation of the descriptive terms. The main purpose of our work is to define a methodology to build a standardized visual coding system unambiguously characterizing the terms of a microglossary. The methodology follows two steps: 1) the acquisition of experts' descriptions of images using the microglossary and 2) a consensus derivation. The procedure was applied on a set of 85 histopathological images of breast tumors described by two experts. Among the 339 objects selected in images, 176 were detected by both experts, 77% localized at the same place and 25% also identically labeled. The microglossary was enriched and illustrated via the resulting consensual descriptions. The contribution of this work supports relevant indexing of biomedical images and image-related information.

Breast Neoplasms↗
PubMed1998

IDEM: a Web application of case-based reasoning in histopathology.

Different software engineering and artificial intelligence methods can be used to design Internet retrieval of prototypical medical images. We used the case-based reasoning (CBR) approach to provide an 'intelligent' access to a collection of illustrated medical cases through the Internet. This paper presents a Web interface for the CBR system IDEM (image and diagnosis from examples in medicine) in the domain of breast pathology. Thanks to the definition of a similarity measure between the descriptions of cases we propose a flexible querying of the case-base and a quantitative browsing among cases through similarity links. The resemblance rates provided by the system argue for the quality and the relevancy of the retrieved data. The flexibility of the querying process is robust to missing information and could be adapted to a daily practice. The CBR approach is a promising method for a clinical relevant and an efficient retrieval of reference images and diagnosis clues through Internet.

Artificial Intelligence↗
PubMed1998

Case based diagnosis in histopathology of breast tumours.

Relevant knowledge and decision making process in histopathology is mostly included in typical pathological cases encountered by the expert. In this article we address the issue of exploiting this knowledge in the diagnosis process. We present the first steps of a Case-Based-Reasoning (CBR) system that uses the previously resolved pathological cases in order to facilitate decision making and diagnosis formulation of a new case. The work has been performed in two phases. Firstly, an object-oriented model of the domain was developed and 35 pathological cases of breast tumours were represented within this model. Secondly, the functional architecture of the CBR system was designed and the main procedure, the selection of similar cases, was achieved. The selection procedure is based on an original similarity measure that takes into account both semantic and structural resemblances and differences between the cases. A first evaluation of the system was performed on several cases of the data base. The interest of the CBR approach in situations where heuristic rules cannot be clearly defined is discussed.

Algorithms↗