An efficient multi-RVM classification-based ultrasound lung image retrieval approach

Abstract

This paper proposes an efficient Multi-Relevance Vector Machine (multi-RVM) classification-based ultrasound image retrieval approach for retrieval of lung images relevant to the query image. In our proposed work, a hybrid median filter is used for filtering the training and testing ultrasound lung image. Extraction of feature in the lung image is performed by using the Tamura features and convoluted grey-level co-occurrence matrix approach. The particle swarm optimisation combined differential evolution feature selection approach performs selection of minimum set of features relevant to the query image. Multi-RVM-based classification technique is used to identify the types of lung diseases. Finally, the Hamming-distance-based retrieval technique performs retrieval of similar and relevant lung images from the database. From the performance analysis result, it is clearly evident that the proposed approach achieves better performance in terms of accuracy, sensitivity and specificity, when compared to the existing classification techniques.