MedFMI-SiR: A Powerful DBMS Solution for Large-Scale Medical Image Retrieval

Kaster, Daniel S.; Bugatti, Pedro Henrique; Ponciano-Silva, Marcelo; Traina, Agma J. M.; Marques, Patrícia Angélica Alves; Santos, Antonio Cardoso dos; Traina, Caetano

doi:10.1007/978-3-642-23208-4_2

Cited by 15 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the content retrieval operations are performed still at the application level, as similarity queries are not naturally supported by commercial RDBMS. On the other hand, recent studies have designed prototypes to extend such core routines [23,10], where the user can straightforwardly pose similarity queries into a RDBMS using a proper relational language. The next section reviews the CBMIR Higiia that uses an extended structured query language to perform the operations of image storage, deletion, update, and querying.…”

Section: Definition 1 Feature Extractor Methods (Fem): Given An Ex-mentioning

confidence: 99%

“…According to the ontology proposed by Deserno et al [24], semantic gap is a situation in which the system does not follow the radiologists' sense of similarity. For CBMIR systems based on the metric space approach, the similarity queries are always performed following the perceptual parameters [10,23]. Therefore, non-similar medical images are recovered mainly due to the incorrect setting of perceptual parameters.…”

Section: Definition 1 Feature Extractor Methods (Fem): Given An Ex-mentioning

confidence: 99%

See 1 more Smart Citation

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

et al. 2015

Self Cite

View full text Add to dashboard Cite

Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.

show abstract

Section: Definition 1 Feature Extractor Methods (Fem): Given An Ex-mentioning

confidence: 99%

Section: Definition 1 Feature Extractor Methods (Fem): Given An Ex-mentioning

confidence: 99%

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The system yields an extension [12] that uses the core previously built in order to index and retrieve medical images by content. That application is an example of how a generic information retrieval system can fit a medical context.…”

Section: Introduction and Related Workmentioning

confidence: 99%

MedInject: A General-Purpose Information Retrieval Framework Applied in a Medical Context

Carvalho

Seraphim

et al. 2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

Self Cite

View full text Add to dashboard Cite

The continuous improvement of medical software and instrumentation have contributed to generate large amounts of medical image data. Thus, plenty of ContentBased Image Retrieval systems have emerged in order to index and retrieve images according to similarity criteria. Some of those systems are applied in very specific domains, such as mammography, lung or spine exams. Others, however, are general-purpose applications that can be adopted in a medical environment. In such context, we realized those specific systems could benefit from the facilities brought by generic frameworks and propose our solution. This article presents a novel information retrieval core framework that performs both indexing and similarity search operations over medical image data sets. The framework follows a modular architecture based on Design Patterns and can be easily extended, allowing to other system developers to take advantages of its functions by using the provided interfaces. We performed extensive experiments evaluating several of its properties and target abstractions using medical real data, and show that it allows the implementation to achieve proper similarity retrieval and significant performance improvements in relation to the existing alternatives.

show abstract

“…One of the challenges is designing web-search tools in order to obtain digital images according to the different attributes used when issuing a diagnosis. Although several systems are available [6], [7], [8], most of them are of general purpose, even if they are tailored to a thematic image collection [9].…”

Section: Introductionmentioning

confidence: 99%

MammoApplet: An interactive Java applet tool for manual annotation in medical imaging

Mata

Oliver

Torrent

et al. 2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

View full text Add to dashboard Cite

Abstract-Web-based applications in computational medicine have become increasingly important during the last years. The rapid growth of the World Wide Web supposes a new paradigm in the telemedicine and eHealth areas in order to assist and enhance the prevention, diagnosis and treatment of patients. Furthermore, training of radiologists and management of medical databases are also becoming increasingly important issues in the field. In this paper, we present MammoApplet, an interactive Java applet interface designed as a web-based tool. It aims to facilitate the diagnosis of new mammographic cases by providing a set of image processing tools that allow a better visualization of the images, and a set of drawing tools, used to annotate the suspicious regions. Each annotation allows including the attributes considered by the experts when issuing the final diagnosis. The overall set of overlays is stored in a database as XML files associated with the original images. The final goal is to obtain a database of already diagnosed cases for training and enhancing the performance of novice radiologists.

show abstract

MedFMI-SiR: A Powerful DBMS Solution for Large-Scale Medical Image Retrieval

Cited by 15 publications

References 15 publications

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy

MedInject: A General-Purpose Information Retrieval Framework Applied in a Medical Context

MammoApplet: An interactive Java applet tool for manual annotation in medical imaging

Contact Info

Product

Resources

About