An Efficient System for Medical Image Retrieval using Generalized Gamma Distribution

An Efficient System for Medical Image Retrieval using Generalized Gamma Distribution

Автор: T.V. Madhusudhana Rao, S.Pallam Setty, Y.Srinivas

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 6 vol.7, 2015 года.

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Efficient diagnosis plays a crucial role for treatment. In many cases of criticalness, radiologists, doctors prefer to the usage of internet technologies in order to search for similar cases. Accordingly in this paper an effective mechanism of Content Based Image Retrieval (CBIR) is presented, which helps the radiologists/doctors in retrieving similar images from the medical dataset. The paper is presented by considering brain medical images from a medical dataset. Feature vectors are to be extracted efficiently so as to retrieve the images of interest. In this paper a two-way approach is adopted to retrieve the images of relevance from the dataset. In the first step the Probability Density Functions (PDF) are extracted and in the second step the relevant images are extracted using correlation coefficient. The accuracy of the model is tested on a database consisting of 1000 MR images related to brain. The effectiveness of the model is tested using Precision, Recall, Error rate and Retrieval efficiency. The performance of the proposed model is compared to Gaussian Mixture Model (GMM) using quality metrics such as Maximum distance, Mean Squared Error, Signal to Noise Ratio and Jaccard quotient.

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Generalized Gamma Distribution, Content Based Image Retrieval, Relevancy, Correlation, Quality metrics

Короткий адрес: https://sciup.org/15013882

IDR: 15013882

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