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

Authors: V. Senthilkumar; M. Ezhilarasi

Addresses: Department of Electronics and Communication, RVS Faculty of Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India ' Department of Electronics and Instrumentation, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India

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.

Keywords: CGLCM; convoluted grey-level co-occurrence matrix; Gabor filter; Hamming distance; HMF; hybrid median filter; image retrieval; multi-RVM; relevance vector machine; PSO-DEFS approach; particle swarm optimisation; PSO; differential evolution; feature selection; Tamura features; ultrasound images; lung images; madical imaging; image classification.

DOI: 10.1504/IJBET.2016.079485

International Journal of Biomedical Engineering and Technology, 2016 Vol.22 No.3, pp.189 - 215

Received: 13 Oct 2015
Accepted: 08 Feb 2016

Published online: 29 Sep 2016 *

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