Combining Textual and Visual Ontologies to Solve Medical Multimodal Queries

Radhouani, Saïd; Lim, Joo‐Hwee; Chevallet, Jean–Pierre; Falquet, Gilles

doi:10.1109/icme.2006.262915

Cited by 13 publications

(9 citation statements)

References 11 publications

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“…General ontologies would be suitable for broad queries (Bhogal et al 2007). In previous research efforts involving external knowledge sources, Voorhees (1994) and Liu et al (2004) utilize general ontologies while Radhouani et al (2006) use domain specific ontologies.…”

Section: External Knowledge Based Query Expansion Modelsmentioning

confidence: 99%

“…• Domain-Specific (Radhouani et al, 2006) • General (Voorhees, 1994, Liu et al, 2004 Linguistic Processing…”

Section: Ontologymentioning

confidence: 99%

“…Domain specific and general ontologies have been utilized for the purpose of extracting semantically related terms and sense disambiguation (Liu et al 2004;Voorhees 1994), phrase extraction (Liu et al 2004) and query dimension identification (Radhouani et al 2006) in the external knowledge based query expansion efforts discussed above. Query structures and varying query lengths were not differentiated during the process of expansion in these efforts.…”

Section: Analysis and Open Issuesmentioning

confidence: 99%

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Natural language technology and query expansion: issues, state-of-the-art and perspectives

Selvaretnam

Belkhatir

2011

J Intell Inf Syst

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The availability of an abundance of knowledge sources has spurred a large amount of effort in the development and enhancement of Information Retrieval techniques. Users' information needs are expressed in natural language and successful retrieval is very much dependent on the effective communication of the intended purpose. Natural language queries consist of multiple linguistic features which serve to represent the intended search goal. Linguistic characteristics that cause semantic ambiguity and misinterpretation of queries as well as additional factors such as the lack of familiarity with the search environment affect the users' ability to accurately represent their information needs, coined by the concept "intention gap". The latter directly affects the relevance of the returned search results which may not be to the users' satisfaction and therefore is a major issue impacting the effectiveness of information retrieval systems. Central to our discussion is the identification of the significant constituents that characterize the query intent and their enrichment through the addition of meaningful terms, phrases or even latent representations, either manually or automatically to capture their intended meaning. Specifically, we discuss techniques to achieve the enrichment and in particular those utilizing the information gathered from statistical processing of term dependencies within a document corpus or from external knowledge sources such as ontologies. We lay down the anatomy of a generic linguistic based query expansion framework and propose its module-based decomposition, covering topical issues from query processing, information retrieval, computational linguistics and ontology engineering. For each of the modules we review state-of-the-art solutions in the literature categorized and analyzed under the light of the techniques used.

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Section: External Knowledge Based Query Expansion Modelsmentioning

confidence: 99%

“…• Domain-Specific (Radhouani et al, 2006) • General (Voorhees, 1994, Liu et al, 2004 Linguistic Processing…”

Section: Ontologymentioning

confidence: 99%

Section: Analysis and Open Issuesmentioning

confidence: 99%

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Natural language technology and query expansion: issues, state-of-the-art and perspectives

Selvaretnam

Belkhatir

2011

J Intell Inf Syst

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“…Several examples of studies including non-image data have already been described [122,123]. Textual information has also been used to complement several studies that were part of the ImageCLEF medical challenge or used the same data [141][142][143][144][145][146][147].…”

Section: Retrieval Enhancement Using Non-image Datamentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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“…Through a conceptual indexing process, each document doc is represented by a set of concepts: doc = {c}. Our approaches for conceptual indexing and the underlying results are detailed in our previous works: multilingual text retrieval [10][11], Image retrieval [12] and video retrieval [16].…”

Section: B Multi-dimensional Document Indexingmentioning

confidence: 99%

FlexIR: a Domain-Specific Information Retrieval System

2009

Self Cite

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Abstract-We present a precise search engine adapted to professional environments which are characterized by a domain (e.g. medicine, law, sport, and so on). In our approach, each domain has its own terminology (i.e. a set of terms that denote its concepts: team, player, etc.) and it is organized along dimensions, such as person, location, etc. The dimensions, as described below, are made of concepts and semantic relationships that represent a particular perspective or point of view on the domain. We mainly use the notion of domain dimension to: i) precisely index document content, and ii) develop an interactive interface which allows the user to precisely describe his or her information need and therefore precisely access the document collection.Index Terms-Information retrieval, domain dimensions, user interface.

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Combining Textual and Visual Ontologies to Solve Medical Multimodal Queries

Cited by 13 publications

References 11 publications

Natural language technology and query expansion: issues, state-of-the-art and perspectives

Natural language technology and query expansion: issues, state-of-the-art and perspectives

A graph-based approach for the retrieval of multi-modality medical images

FlexIR: a Domain-Specific Information Retrieval System

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