Abstract
Background: Content Based Image Retrieval (CBIR) has always been a demanding research area as it involves searching of digital images from the collection of images. Difficulty exists in retrieving images for the query posed by concentrating on the factors; computational complexity and accuracy. Though many research works span around CBIR, Content Based Medical Image Retrieval (CBMIR) plays a vital role in the area of medical diagnosis.
Methods: This paper introduces an enhanced feature extraction and retrieval phase for MRI images using Edge based GLCM (EGLCM), and artificial bee colony (ABC) based Artificial Neural Network (ANN) to generate the best results and to yield a higher retrieval accuracy. Discussion: Our proposed work has four important phases namely Pre-Processing, Feature Extraction, Optimized Retrieval using ABC based ANN. and GLCM. GLCM generates the co-occurrence matrix to calculate Homogeneity, Energy, Correlation, and Contrast as texture features. As a result, eight features act as a feature vector to depict images. Then, to optimize the retrieval task, conjunction with ABC based ANN uses Association rule mining with their significant features in which we carry out Training and Validation phases in 1000 and 100 MRI images. Conclusion: The performance of the method envisaged in this paper is encouraging, and the method is cost effective as compared to the methods described in the literature, based on the performance metrics namely precision, accuracy and recall. A comparative graph is given to corroborate the same.Keywords: Artificial bee colony algorithm, artificial neural network (ANN), content based image retrieval (CBIR), content based medical image retrieval (CBMIR), grey level co-occurrence matrix (GLCM), local gabor XOR pattern.
Graphical Abstract
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