An efficient retrieval using edge GLCM and association rule mining guided IPSO based artificial neural network

Abstract

Content Based Image Retrieval (CBIR) is a challenging research area due to increase in multimedia database and other image libraries day by day. With an intent to provide an efficient search and retrieval, we propose an enhanced Content Based Medical Image Retrieval (CBMIR) system to support the medical practitioners in their diagnosis task. For which, we introduce boosted feature extraction and retrieval phase for medical images using Edge GLCM (EGLCM) and Association Rule Mining (ARM) integrated with Artificial Neural Network (ANN). Improved Particle Swarm Optimization (IPSO) is deployed to optimize the weights of ANN. The system is put forth with four important phases; 1. Pre-Processing, 2. Feature Extraction using Edge Histogram Descriptor (EHD), Local Gabor XOR Pattern (LGXP) and EGLCM, 3. Association Rule Mining using Apriori and 4. Optimized Retrieval using IPSO based ANN and Euclidean distance. In ANN, 7,000 images are trained and 1,100 images are tested. On Comparison with the existing systems, our method has shown best results with improved accuracy of 95 % in addition to reduced computational complexity by pre-processing and dimensionality reduction through minimal feature vector.