Neural Network and Genetic Algorithm Based Hybrid Model for Content Based Mammogram Image Retrieval

Jose, T. J. Benedict; Mythili, P.

doi:10.3923/jas.2009.3531.3538

Cited by 10 publications

(1 citation statement)

References 12 publications

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“…Recently there had an attempt to combine these two technologies. As mentioned in the paper [6], they proposed a hybrid model for content based mammogram retrieval which is demonstrated as two stages, and in first stage they dealt with Self organising Map (SOM) neural network for clustering images and in second stage used Genetic algorithm search. But in this paper, the first stage genetic algorithm is used for local optimal result and this result is trained to an optimal result by using neural network.…”

Section: Related Workmentioning

confidence: 99%

Feature Based Image Retrieval Algorithm

Nimi

Tripti

2011

Advances in Computing and Communications

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Abstract. The developments in the field of internet allow users in almost all the professional areas for exploiting the opportunities offered by the ability to access and manipulate remotely-stored images. The large multimedia database has to be processed within a small fraction of seconds for many of the real time applications. This demand of using the technique of content based image retrieval (CBIR) as a scheme for searching large database for image retrieval has addressed some of the issues that need to be solved for having an efficient system. The paper focuses on the issues of image retrieval and also suggests a method to get an accurate result by using a hybrid search methodology. The paper works in two phases-in the first phase it works with genetic algorithm to get a local optimal result and in the second phase, it works with neural network to get a global optimal result.

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Section: Related Workmentioning

confidence: 99%

Feature Based Image Retrieval Algorithm

Nimi

Tripti

2011

Advances in Computing and Communications

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Neighbourhood search feature selection method for content-based mammogram retrieval

Chandy

Christinal

Theodore

et al. 2016

Med Biol Eng Comput

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Content-based image retrieval plays an increasing role in the clinical process for supporting diagnosis. This paper proposes a neighbourhood search method to select the near-optimal feature subsets for the retrieval of mammograms from the Mammographic Image Analysis Society (MIAS) database. The features based on grey level cooccurrence matrix, Daubechies-4 wavelet, Gabor, Cohen-Daubechies-Feauveau 9/7 wavelet and Zernike moments are extracted from mammograms available in the MIAS database to form the combined or fused feature set for testing various feature selection methods. The performance of feature selection methods is evaluated using precision, storage requirement and retrieval time measures. Using the proposed method, a significant improvement is achieved in mean precision rate and feature dimension. The results show that the proposed method outperforms the state-of-the-art feature selection methods.

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