Image content modeling for neuroscience databases

Nah, Yunmook; Sheu, Phillip C.‐Y.

doi:10.1145/568760.568778

Cited by 7 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past decade, researchers have been developing several prominent content-based image retrieval (CBIR) systems for medicine [12], [13], [25], [27], [37], [47]. These CBIR systems mimic the domain knowledge to extract image contents and provide query methods for direct image (visual pattern) match using low level image features.…”

mentioning

confidence: 99%

“…Fast query results for nearest neighbor search is addressed by Korn et al [27] who used multidimensional indexing of medical tumors with similar shapes using an R-tree. The system proposed by Nah and Sheu [37] used operational semantics to ensure the meaningfulness of content-based retrieval of neuroscience images. In the ASSERT system [47], Shyu et al designed a suite of computer vision algorithms to extract visual abnormalities and used multidimensional hashing approach to index pathologies of lung HRCT images.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Knowledge Representation and Sharing Using Visual Semantic Modeling for Diagnostic Medical Image Databases

Barb

Shyu²,

Sethi

2005

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

Abstract-Information technology offers great opportunities for supporting radiologists' expertise in decision support and training. However, this task is challenging due to difficulties in articulating and modeling visual patterns of abnormalities in a computational way. To address these issues, well established approaches to content management and image retrieval have been studied and applied to assist physicians in diagnoses. Unfortunately, most of the studies lack the flexibility of sharing both explicit and tacit knowledge involved in the decision making process, while adapting to each individual's opinion. In this paper, we propose a knowledge repository and exchange framework for diagnostic image databases called "evolutionary system for semantic exchange of information in collaborative environments" (Essence). This framework uses semantic methods to describe visual abnormalities, and offers a solution for tacit knowledge elicitation and exchange in the medical domain. Also, our approach provides a computational and visual mechanism for associating synonymous semantics of visual abnormalities. We conducted several experiments to demonstrate the system's capability of matching synonym terms, and the benefit of using tacit knowledge in improving the meaningfulness of semantic queries.Index Terms-Content-based image retrieval, knowledge exchange, knowledge representation, medical image database, radiology, semantic query.

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Knowledge Representation and Sharing Using Visual Semantic Modeling for Diagnostic Medical Image Databases

Barb

Shyu²,

Sethi

2005

IEEE Trans. Inform. Technol. Biomed.

View full text Add to dashboard Cite

show abstract

“…As such, concepts can be modeled using similar techniques. We have previously applied this image content modeling technique to brain tissue images to represent its contents in terms of classes and relationships as shown in Figure 2 [6]. More detailed conceptual knowledge can be represented using a conceptual knowledge hierarchy, consisting of concept types and concept values.…”

Section: Conceptual Knowledge Hierarchymentioning

confidence: 99%

Semantic biological image management and analysis

Chubb

Inagaki

Cotman

et al.

Proceedings. 15th IEEE International Conference on Tools With Artificial Intelligence

View full text Add to dashboard Cite

Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research.It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper discusses the feasibility of developing a set of imaging algorithms that allows the user to train an imaging system/database to recognize biological objects of various sorts based on their own criteria. The image software builds a model of the selected objects by reiterative training, evolving the ability (i.e., the underlying rules) to recognize these objects. These objects are in turn archived into a growing database that builds upon the experience of multiple individuals that can be referred to as an object zoo. The user can search these zoos using newly acquired images based on similarly using more narrow or broadened criteria based on a new, semantic database framework called Semantic Objects.

show abstract