An integrated Framework Based on Texture Features, Cuckoo Search and Relevance Vector Machine for Medical Image Retrieval System

Abstract

As medical images are widely used in healthcare applications, Content Based Medical Image Retrieval (CBMIR) system is needed for physicians to convey effective decisions to patients and for medical research students to learn imaging characteristics for their extensive research based on visual features. However the performance of the retrieval is restricted due to high feature dimensionality of visual features. To reduce the high feature dimension, an integrated approach is proposed such as Visual feature extraction, Feature selection, Feature Classification and Similarity measurements. The selected feature is texture features by using Local Binary Patterns (LBP) in which extracted texture features are designed as feature vector database. Fuzzy based Cuckoo Search (FCKS) techniques are applied for feature selection to reduce the high feature vector dimensionality and addresses the difficulty of feature vectors being surrounded in local feature optima also the global optimum feature position to be special for all feature cuckoo hosts. Fuzzy based Relevance Vector Machine (FRVM) classification is an proficient method to customize the collections of relevant image features that would classify dimensionally determined optimized feature vectors of images. The Euclidean Distance (ED) is a standard technique for similarity measurement between the query image and the image database. The proposed system is implemented on thousands of medical images and achieved a high retrieval precision and recall compared with other two methods as validated through experiments.