Recuperação de imagem baseada em conteúdo: uso de atributos de textura para caracterização de microcalcificações mamográficas

Azevedo-Marques, Paulo Mazzoncini de; Honda, Marcelo Hossamu; Rodrigues, Jose; Santos, Rildo Ribeiro dos; Traina, Agma J. M.; Júnior, Caetano Traina; Bueno, Josiane Maria

doi:10.1590/s0100-39842002000200009

Cited by 7 publications

(3 citation statements)

References 19 publications

(23 reference statements)

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“…Besides evaluation based on diagnosis and anatomical region, analysis of visually similar cases, although with different diagnosis, result in an improvement of the educational quality (24) . Content-based images retrieval is one of the computational vision techniques more intensely studied in the last tem years, and is based on three classes of visual characteristics: color, texture and shape (25) . These attributes allow the development of robust computational tools capable of characterizing images by their own contents, adding advantages to the images identification based only on textual descriptors that constitute the traditional classification of medical images files (23) .…”

Section: Content-based Medical Images Retrievalmentioning

confidence: 99%

Grades computacionais na recuperação de imagens médicas baseada em conteúdo

2007

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OBJECTIVE: To utilize the grid computing technology to enable the utilization of a similarity measurement algorithm for content-based medical image retrieval. MATERIALS AND METHODS: The content-based images retrieval technique is comprised of two sequential steps: texture analysis and similarity measurement algorithm. These steps have been adopted for head and knee images for evaluation of accuracy in the retrieval of images of a single plane and acquisition sequence in a databank with 2,400 medical images. Initially, texture analysis was utilized as a pre-selection resource to obtain a set of the 1,000 most similar images as compared with a reference image selected by a clinician. Then, these 1,000 images were processed utilizing a similarity measurement algorithm on a computational grid. RESULTS: The texture analysis has demonstrated low accuracy for sagittal knee images (0.54) and axial head images (0.40). Nevertheless, this technique has shown effectiveness as a filter, pre-selecting images to be evaluated by the similarity measurement algorithm. Content-based images retrieval with similarity measurement algorithm applied on these pre-selected images has demonstrated satisfactory accuracy -0.95 for sagittal knee images, and 0.92 for axial head images. The high computational cost of the similarity measurement algorithm was balanced by the utilization of grid computing. CONCLUSION: The approach combining texture analysis and similarity measurement algorithm for content-based images retrieval resulted in an accuracy of > 90%. Grid computing has shown to be essential for the utilization of similarity measurement algorithm in the content-based images retrieval that otherwise would be limited to supercomputers.

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Section: Content-based Medical Images Retrievalmentioning

confidence: 99%

Grades computacionais na recuperação de imagens médicas baseada em conteúdo

2007

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“…In the context of mammography, some works have explored the use of CBIR. Azevedo-Marques et al [6] developed a mammographic image retrieval system for use as a diagnostic aid; they proposed the use of visual features obtained from Haralick's measures of texture [7] for the characterization of regions of interest (ROIs) containing microcalcifications. Alto et al [8] proposed the use of texture, gradient (edgesharpness), and shape measures as indices for quantitative representation of breast masses in mammograms, and studied their application for CBIR.…”

Section: Introductionmentioning

confidence: 99%

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

et al. 2008

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Object We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms. Materials and methods Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval. ResultsThe results indicate improvement due to RFb of up to 62% in precision and 39% in RWP. ConclusionThe gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.

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“…Os extratores de textura foram implementados a partir do cálculo da matriz de coocorrência da imagem. Conforme definem Azevedo- Marques et al (2002) e Ferrero et al (2006), a matriz de coocorrência correspondeà frequência de um nível de cinza na imagem A considerando uma distância em uma determinada direção. A posição p(i,j) indica a frequência de ocorrência de um particular par de nível de cinza i e j, obtido a partir de uma distância e de umângulo (direção).…”

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Processamento gráfico para aplicações em saúde: técnicas, requisitos, ferramentas, desafios e oportunidades

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Recuperação de imagem baseada em conteúdo: uso de atributos de textura para caracterização de microcalcificações mamográficas

Cited by 7 publications

References 19 publications

Grades computacionais na recuperação de imagens médicas baseada em conteúdo

Grades computacionais na recuperação de imagens médicas baseada em conteúdo

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

Processamento gráfico para aplicações em saúde: técnicas, requisitos, ferramentas, desafios e oportunidades

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