A Novel Hybrid Framework for Medical Image Retrieval
DOI:
https://doi.org/10.51983/ajeat-2018.7.2.1014Keywords:
texture autocorrelogram, chordiogram descriptor, Color autocorrelogramAbstract
A new hybrid framework for Content-Based Medical Image Retrieval (MCBIR) is proposed in this paper to deals with the accuracy issues related with the existing MCBIR. The proposed hybrid framework initially divides the images into number of non-overlapping rectangular regions. Subsequently, statistical based color autocorrelogram (CA) and texture autocorrelogram (TA) is extracted for each region respectively. Then the geometric based chordiogram descriptor (CD) is extracted for each region. Both the statistical based and geometric based descriptors are combined to create a feature vector. The corresponding image regionsor patches in the query and target medical images are compared using the Canberra distance measure. The proposed hybrid framework is evaluated using the benchmark database and it is confirmed that it significantly outperforms the state-of-the-art system in terms precision, recall and G-measure.
References
H. Müller et al., "Benefits of Content-based Visual Data Access in Radiology," Radio Graphics, vol. 25, no. 3, pp. 849-858, 2005.
H. L. Tang, R. Hanka, and H. H. Ip, "Histological image retrieval based on semantic content analysis," IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 1, pp. 26-36, 67, 2003.
T. M. Lehmann et al., "Automatic categorization of medical images for content-based retrieval and data mining," Computerized Medical Imaging and Graphics, vol. 29, pp. 143-155, 2005.
M. Rahman et al., "Multimodal biomedical image retrieval using hierarchical classification and modality fusion," Int J Multimed Info Retr., vol. 2, pp. 159-173, 2013.
M. S. Sudhakar and B. K. Bagan, "An effective biomedical image retrieval framework in a fuzzy feature space employing Phase Congruency and GeoSOM," Applied Soft Computing, article in press, 2014.
S. Murala and Q. M. J. Wu, "MRI and CT image indexing and retrieval using local mesh peak valley edge patterns," Signal Processing, vol. 29, no. 93, pp. 400-409, 2014.
S. Murala and Q. M. J. Wu, "Local ternary co-occurrence patterns: A new feature descriptor for MRI and CT image retrieval," Neurocomputing, vol. 119, pp. 399-412, 2013.
P. Banerjeea et al., "Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval," Expert Systems with Applications, vol. 113, pp. 100-115, 2018.
A. Kumar et al., "A graph-based approach for the retrieval of multi-modality medical images," Medical Image Analysis, vol. 18, no. 2, pp. 330-342, 2014.
K. Seetharaman and S. Sathiamoorthy, "A unified learning framework for content-based medical image retrieval using a statistical model," Journal of King Saud University-Computer and Information Sciences, vol. 28, no. 1, pp. 110-124, 2016.
G. H. Liu et al., "Image retrieval based on micro-structure descriptor," Pattern Recognition, vol. 44, no. 9, pp. 2123-2133, 2011.
K. Seetharaman and S. Sathiamoorthy, "An Improved Edge Direction Histogram and Edge Orientation AutoCorrlogram for an Efficient Color," in Proceedings of International Conference on Advanced Computing and Communication Systems (ICACCS -2013), Coimbatore, India, Dec. 19-21, 2013.
K. Seetharaman and S. Sathiamoorthy, "A Novel scheme for texture feature characterization using a statistical approach," in Proceedings of International Conference on Advanced Computing and Communication Systems (ICACCS -2017), Coimbatore, India, 6-7 Jan. 2017.
K. Seetharaman, "Texture analysis based on a family of stochastic models," in Signal and Image Processing Applications (ICSIPA), IEEE International Conference, pp. 518-523, 2009.
K. Seetharaman and M. Kamarasan, "Statistical framework for image retrieval based on multiresolution features and similarity method," Multimedia Tools and Applications, vol. 3, no. 1, pp. 53-66, 2014.
A. Q. S. M. Anwar et al., "Medical image retrieval using deep convolutional neural network," Neurocomputing, vol. 266, pp. 8-20, 2017.
V. P. Singh and R. Srivastava, "Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map," Bio Cybernetics and Biomedical Engineering, vol. 38, no. 1, pp. 90-105, 2018.
X. Wang et al., "A chordiogram image descriptor using local edgels," J. Vis. Commun. Image R., vol. 49, pp. 129–140, 2017.
Y. D. Chun et al., "Content-Based Image Retrieval using Multiresolution Color and Texture Features," IEEE Transactions on Multimedia, vol. 10, no. 6, pp. 1073-1084, 2008.
G. Qiu and K. M. Lam, "Frequency layered color indexing for content-based image retrieval," IEEE Trans. Image Process., vol. 12, no. 1, pp. 102–113, 2003.
K. Iqbal et al., "Content-based image retrieval approach for biometric security using color, texture and shape features controlled by fuzzy heuristics," Journal Comput. Syst. Sci., vol. 78, pp. 1258-1277, 2012.
G. H. Liu and J. Y. Yang, "Content-based image retrieval using color difference histogram," Pattern Recognition, vol. 46, pp. 188-198, 2013.
Y. B. R. Charles and R. Ramra, "International Journal of Electronics and Communications (AEÜ)," vol. 70, no. 3, pp. 225-233, 2016.
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