Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy
| Autor: | Davi Pereira dos Santos, Caetano Traina, Marcelo Ponciano-Silva, Paulo Mazzoncini de Azevedo-Marques, André Ponce de Leon F. de Carvalho, Marcos V. N. Bedo |
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| Rok vydání: | 2015 |
| Předmět: |
Databases
Factual Computer science media_common.quotation_subject Information Storage and Retrieval 02 engineering and technology Article Pattern Recognition Automated 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Perception Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans RECONHECIMENTO DE IMAGEM Radiology Nuclear Medicine and imaging Image retrieval Extreme learning machine media_common Information retrieval Radiological and Ultrasound Technology Result set Computer Science Applications Computer-aided diagnosis Female 020201 artificial intelligence & image processing Ensemble strategy Classifier (UML) Mammography Semantic gap |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| ISSN: | 1618-727X 0897-1889 |
| DOI: | 10.1007/s10278-015-9809-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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