Abstract

In this paper, we are going to present the multiclass medical image content-based image retrieval system based on classification and clustering. Images are segmented using hill climbing-based segmentation based on the extracted visual features. In the improved HCBS technique, a clustering that is based on kernel-based fuzzy C-means is employed. In the next step, features like color, texture, edge density, region area, and visual words from the segmented images are extracted. The visual word can be extracted by using the clustering techniques. This visual word represents the uniqueness of the medical image, and it is used for better classification. Then, the image can be classified by using an optimal classifier artificial neural network based on the firefly algorithm. This classification leads to filtering out the irrelevant images from the database and reduces the search space for further retrieval process. In the second stage, the relevant images are extracted from the reduced database based on the similarity measure. The proposed CBIR technique is assessed by querying different images, and the retrieval efficiency is estimated by determining the precision-recall values for the retrieval results.