Method for Medical Image Retrieval Based on Frequency Layer Feature

Wu, Jianhua; Wang, Xiaoman; Yan, Donghui; Wei, Chaorong; Zhang, Yixiang

doi:10.1109/itcs.2009.321

Cited by 5 publications

(10 citation statements)

References 7 publications

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“…We design experiments on ImageCLEFmed and medical image data set used by Wu et al (2009), which will answer the following questions:…”

Section: Methodsmentioning

confidence: 99%

“…In this section, we will first introduce the ImageCLEFmed medical image data set and retrieval accuracy metric briefly in Section 4.1, and then we compare ILMDA to the state-of-the-art dimensionality reduction methods using a subset of ImageCLEFmed in Section 4.2, at last we will see how our dimensionality reduction methods work with different image features and retrieval schemes, using both the two data sets: ImageCLEFmed and Wu et al (2009) It currently contains 50,026 arbitrarily selected anonymous radiographic images taken randomly from patients of different ages, genders, and pathologies during medical routine. The images are categorised into 116 classes according to the IRMA code (Lehmann et al, 2003).…”

Section: Methodsmentioning

confidence: 99%

“…Principal Component Analysis (PCA) is a most widely used method in medical image feature dimensionality reduction (Rahman et al, 2007;Greenspan and Pinhas, 2007;Liu et al, 2010;Wu et al, 2009). Principal Component Analysis (PCA) is a most widely used method in medical image feature dimensionality reduction (Rahman et al, 2007;Greenspan and Pinhas, 2007;Liu et al, 2010;Wu et al, 2009).…”

Section: Overview Of the Medical Image Retrieval Schemementioning

confidence: 99%

“…The two medical image retrieval system are described in Rahman et al (2007) and Wu et al (2009) separately. The two medical image retrieval system are described in Rahman et al (2007) and Wu et al (2009) separately.…”

Section: Performance In Medical Image Retrieval Systemmentioning

confidence: 99%

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Iterated Large-Margin Discriminant Analysis for feature Dimensionality Reduction in medical image retrieval

Wang

Marchiori

et al. 2011

IJBET

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Feature Dimensionality Reduction (DR) is an important topic in medical image retrieval. However, most medical image retrieval systems use naive principal component analysis for DR, which is not optimal. Recently, the class conditional nearest neighbour relation over pairs of points was introduced. In this paper, we extend it to class conditional K-nearest neighbour (ccknn). Based on ccknn, we define the within-class and between-class graph for Large-Margin Discriminat Analysis (LMDA). Moreover, an iterative expectation-maximisation framework is applied to LMDA to boost the performance. The experimental results demonstrate that the proposed approach yields significant improvements over the state-of-the-art DR algorithms.

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“…We design experiments on ImageCLEFmed and medical image data set used by Wu et al (2009), which will answer the following questions:…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Overview Of the Medical Image Retrieval Schemementioning

confidence: 99%

Section: Performance In Medical Image Retrieval Systemmentioning

confidence: 99%

See 2 more Smart Citations

Iterated Large-Margin Discriminant Analysis for feature Dimensionality Reduction in medical image retrieval

Wang

Marchiori

et al. 2011

IJBET

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“…Researchers have addressed the dimensionality reduction problem by applying various algorithms such as Principal Component Analysis, Weighted Multi-Dimensional Scaling, Tabu Search Method (Wu et al, 2009) and Evolutionary Algorithms for optimizing the features are Particle Swarm Optimization (PSO) (Ye et al, 2009), Ant Colony Optimization (ACO) (Piatrik and Izquierdo, 2009), Genetic Algorithms (GA) (Silva et al, 2011), Gravitational Search Algorithm (GSA) (Rashedi et al, 2009). The individual dimensional reduction approach based CBMIR is used to recognize the probable optimal resolutions within a sensible amount of time but the convergence rapidity is declined.…”

Section: Level 2: Optimized Feature Selectionmentioning

confidence: 99%

Feature Dimension Reduction for Efficient Medical Image Retrieval System Using Unified Framework

Jaganathan¹,

Vennila²

2013

Journal of Computer Science

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Feature dimensionality reduction problem is a major issue in Content Based Medical Image Retrieval (CBMIR) for the effective management of medical images with the support of visual features for the purpose of diagnosis and educational research field. The proposed CBMIR is used a unified approach based on extraction of visual features, optimized feature selection, classification of optimized features and similarity measurements. The Texture features are selected using Gray Level Co-occurrence Matrix (GLCM), Tamura Features (TF) and Gabor Filter (GF) in which pull out of features are formed a feature vector database. Fuzzy based PSO (FPSO) is applied for Feature selection to overcome the difficulty of feature vectors being surrounded in local optima of original PSO. This procedure also integrates a smart policymaking structure of ACO procedure into the novel FPSO where the global optimum position to be exclusive for every feature particle. The Fuzzy based Particle Swarm Optimization and Ant Colony Optimization (FPSO-ACO) technique is used to trim down the feature vector dimensionality and classification is accomplished using an extensive Fuzzy based Relevance Vector Machine (FRVM) to form collections of relevant image features that would provide an accepted way to classify dimensionally concentrated feature vectors of images. The Euclidean Distance (ED) is recognized as finest for similarity measurement between the medical query image and the medical image database. This proposed approach can acquire the query from the user and had retrieved the desired images from the database. The retrieval performance would be assessed based on precision and recall. This proposed CBMIR is used to provide comfort to the physician to obtain more assurance in their decisions for diagnosis and research students of medicine are keenness to get the crucial images fruitfully for additional investigation of their exploration.

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Method for Medical Image Retrieval Based on Frequency Layer Feature

Cited by 5 publications

References 7 publications

Iterated Large-Margin Discriminant Analysis for feature Dimensionality Reduction in medical image retrieval

Iterated Large-Margin Discriminant Analysis for feature Dimensionality Reduction in medical image retrieval

Feature Dimension Reduction for Efficient Medical Image Retrieval System Using Unified Framework

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