An Efficient Salient Feature based Histology Image Retrieval

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Due to the development of digital microscopy technologies there are huge quantity of histology images are collected and stored in database. The capability to handle and access these collections of histology images is considered as a key constituent for next generation of medical systems. These types of image retrieval system are the combination of different features play major key responsibility to retrieve images. It aims at attractive the expressive authority of visual and salient features equivalent to semantically consequential queries.  Most of the existing work doesn’t  go behind with different combination of features to retrieve digital images along with salient features and visual feature still becomes major problem, extraction of these features also not major problem .To overcome  these issue in this paper presents a improved Gaussian mixture model that routinely extracts both salient and visual representation of features from histology image database . A Gabor filtering framework is proposed, which makes the salient regions further radiant in the saliency maps to retrieve histology images with enhanced image quality results. The major objective of the work is to found the most significant visual representation of features and salient features for a specific keyword that is connected with numerous query image retrieval processes for exact matching of results. The proposed work uses an Enhanced Artificial Fish Swarm Algorithm (AFSA) method, which aims to appreciate an best possible visual-semantic matching purpose by together allowing for the different preference of the collection of query images.  It is capable to discover different types of features that summarize diverse portions of the majority representative of visual patterns and salient features for every conception. Experimental results demonstrate that proposed EAFSA system for content-based histology image retrieval system, high retrieval accurateness other than existing methods.
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Content Based Image Retrieval (CBIR); Histology Image Retrieval; Gaussian Mixture Model; Gabor Filtering; Enhanced Artificial Fish Swarm Algorithm (EAFSA) Optimization

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