Conference Paper

Statistical Association Rules and Relevance Feedback: Powerful Allies to Improve the Retrieval of Medical Images

DOI: 10.1109/CBMS.2006.148 Conference: 19th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2006), 22-23 June 2006, Salt Lake City, Utah, USA
Source: DBLP

ABSTRACT

This work aims at developing an efficient support to improve the precision of medical image retrieval by content, introducing an approach that combines techniques of statistical association rule mining and relevance feedback. Low level features of shape and texture are extracted from images. Statistical association rules are used to select the most relevant features to discriminate the images, reducing the size of the feature vectors and eliminating noisy features that influence negatively the query results, making the whole process more efficient. Additionally, our approach uses a new relevance feedback technique to overcome the semantic gap that exists between low level features and the high level user interpretation of images. Experiments show that the combination of statistical association rule mining and the relevance feedback technique proposed here improve the precision of the query results up to 100%

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    • "This has motivated many researchers to find efficient, effective and accurate algorithms that are domain independent for representation, description and retrieval of images of interest. There have been many algorithms developed to represent, describe and retrieve images using their visual features such as shape, colour and texture [1], [2], [3], [4], [5]. The visual feature representation and description play an important role in image classification, recognition and retrieval. "
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    ABSTRACT: Research is taking place to find effective algorithms for content-based image representation and description. There is a substantial amount of algorithms available that use visual features (color, shape, texture). Shape feature has attracted much attention from researchers that there are many shape representation and description algorithms in literature. These shape image representation and description algorithms are usually not application independent or robust, making them undesirable for generic shape description. This paper presents an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method, the density of feature points within defined rings around the centroid of the image is obtained. The KDFPE is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Density Histogram Feature Points (DHFP) method. Analytic analysis is done to justify our method, which was compared with the DHFP to prove its robustness.
    Full-text · Article · Mar 2012
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    • "Já entre os algoritmos de classificação supervisionada, temos o Support Vector Machine (SVM) [2] e o k-Nearest Neighbors (k-NN) [1], que calcula a distância de uma imagem de entrada para todas as outras presentes na sua base, ordena as imagens da base por esta distância e define como a classe da imagem de entrada a classe mais freqüente nas k primeiras imagens. Dentre as abordagens para seleção de atributos determinantes , existem soluções baseadas em algoritmos comerciais como o C4.5 [8] e outras, acadêmicas, como o algoritmo Statistical Association Rule Miner (StARMiner) [10]. O StARMiner foi originalmente utilizado para classificação de imagens médicas. "
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    ABSTRACT: Resumo Métodos eficientes de busca por imagens são cada vez mais necessários e importantes. Nos últimos anos, fatores como a Internet, a disseminação de câmeras digitais, o au-mento da capacidade de armazenamento e a diminuição do preço deste armazenamento, fizeram com que a quan-tidade de informação visual disponível aumentasse con-sideravelmente. Com isto, surge a necessidade do desen-volvimento de métodos eficientes para recuperação de im-agens em grandes bases de dados. Para alcançar este ob-jetivo é necessário descrever o conteúdo das imagens de forma eficaz e de modo que gere dados que possam ser uti-lizados em uma classificação automática de imagens. Este objetivo é alcançado por meio de atributos relacionados com as características visuais das imagens. Este trabalho visa identificar alguns destes atributos e verificar a eficá-cia dos atributos selecionados utilizando um classificador automático. Como base foi utilizado o acervo de obras do artista Candido Portinari, cujas imagens digitais se en-contram disponíveis para todos e já possui uma classifi-cação manual, com a qual o método proposto pode ser con-frontado.
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    ABSTRACT: In this chapter we discuss how to take advantage of association rule mining to promote feature selection from low-level image features. Feature selection can significantly improve the precision of content-based queries in image databases by removing noisy and redundant features. A new algorithm named StARMiner is presented. StARMiner aims at finding association rules relating low-level image features to high-level knowledge about the images. Such rules are employed to select the most relevant features. We present a case study in order to highlight how the proposed algorithm performs in different situations, regarding its ability to select the most relevant features that properly distinguish the images. We compare the StARMiner algorithm with other well-known feature selection algorithms, showing that StARMiner reaches higher precision rates. The results obtained corroborate the assumption that association rule mining can effectively support dimensionality reduction in image databases.
    No preview · Chapter · Oct 2008
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