Journal of Computer Science

IMAGE RETRIEVAL USING CERTAIN BLOCK BASED DIFFERENCE OF INVERSE PROBABILITY AND CERTAIN BLOCK BASED VARIATION OF LOCAL CORRELATION COEFFICIENTS INTEGRATED WITH WAVELET MOMENTS

B. Thenkalvi and S. Murugavalli

DOI : 10.3844/jcssp.2014.1497.1507

Journal of Computer Science

Volume 10, Issue 8

Pages 1497-1507

Abstract

Content Based Image Retrieval (CBIR) is an evolving topic under image processing. Retrieval on medical images plays a vital role in saving mankind. Medical Content Based Image Retrieval (MCBIR) does not stop its work in displaying similar images; it also goes one step further for diagnosis to decide the method of therapy by comparing the query image and database image. Hence we believe that, our work is a mile stone in the medical imaging research and will gain an appreciable amount of demand in automatic diagnosis done through artificial intelligence. While handling a large amount of data base, retrieval naturally reduces the search area. Many research works were conducted on CBIR systems to yield better performance on retrieval of medical images. Here we propose a modified integrated approach which extracts low level image features: Color, intensity, shape and texture using Certain Block based Difference of Inverse Probability (CBDIP) and Certain Block based Variation of Local Correlation coefficients (CBVLC) for pre-processed images. Consequently wavelet moments are calculated on derived CBDIP and CBVLC values that leads to mining on medical images only with 48 feature vectors. Thereby greatly reducing total number of feature vectors used for similarity comparison for retrieval of similar images. To know the retrieval accuracy, precision and recall values are calculated for the mined 1000 images. It has been examined that this integrated approach shows an improvement in retrieval accuracy and the time taken for similarity comparison.

Copyright

© 2014 B. Thenkalvi and S. Murugavalli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.