An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images
Abstract Various texture based approaches have been proposed for image indexing in bio-medical image processing and a precise description of image for indexing in bio-medical image database has always been a challenging task. In this paper, an extension of local mesh peak valley edge pattern (LMePVEP) has been proposed and its effectiveness is experimentally justified. The proposed algorithm explores the relationship of center pixel with the surrounding ones along with the relationship of pixels amongst each other in five different directions. It is then compared with the original LMePVEP as well as a directional local ternary quantized extrema pattern (DLTerQEP) based approach using two bench mark databases viz. ELCAP database for lungs and Wiki cancer data set for thyroid cancer. Further a live dataset for brain tumor is also used for experimental evaluation. The experimental results show that an average improvement of 11.16% in terms of average retrieval rate (ARR) and 5.37% in terms of average retrieval precision (ARP) is observed for proposed enhanced LMePVEP over conventional LMePVEP.
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Elcap Database: http://www.via.cornell.edu/lungdb.html. Date of last visit: 01st January 2017.
Gibbs, P., & Turnbull, L. W. (2003). Textural analysis of contrast-enhanced MR images of the breast. Magnetic Resonance in Medicine, 50(1), 92-98. DOI: 10.1002/mrm.10496.
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Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern recognition, 42(3), 425-436.
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Murala-2, S., & Wu, Q. J. (2014). MRI and CT image indexing and retrieval using local mesh peak valley edge patterns. Signal processing: image communication, 29(3), 400-409. DOI: 10.1016/j.image.2013.12.002.
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Quddus, A., & Basir, O. (2012). Semantic image retrieval in magnetic resonance brain volumes. IEEE transactions on information technology in biomedicine, 16(3), 348-355. DOI: 10.1109/TITB.2012.2189439.
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Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6), 1635-1650. DOI: 10.1109/TIP.2010.2042645.
Unay, D., Ekin, A., & Jasinschi, R. (2008, October). Medical image search and retrieval using local binary patterns and KLT feature points. In 2008 15th IEEE International Conference on Image Processing (pp. 997-1000) IEEE. DOI: 10.1109/ICIP.2008.4711925.
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Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011). Content-based image retrieval in radiology: current status and future directions. Journal of Digital Imaging, 24(2), 208-222. DOI: 10.1007/s10278-010-9290-9
Antani, S., Long, L. R., & Thoma, G. R. (2004, September). Content-based image retrieval for large biomedical image archives. In Proceedings of 11th World Congress on Medical Informatics (MEDINFO) (7-11).
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7.
Deep, G., Kaur, L., & Gupta, S. (2016). Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval. Engineering Science andTechnology, an International Journal. DOI:10.1016/j.jestch.2016.05. 006.
Deep-2, G., Kaur, L., & Gupta, S. (2016). Biomedical Image Indexing and Retrieval Descriptors: A Comparative Study. Procedia Computer Science,85, 954-961. DOI: 10.1016/j.procs.2016.05.287
Dimitrovski, I., Kocev, D., Kitanovski, I., Loskovska, S., & Džeroski, S. (2015). Improved medical image modality classification using a combination of visual and textual features. Computerized Medical Imaging and Graphics,39, 14-26. DOI: 10.1016/j.compmedimag.2014.06.005
Do, M. N., & Vetterli, M. (2002). Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance. IEEE transactions on image processing, 11(2), 146-158. DOI: 10.1109/83.982822
Elcap Database: http://www.via.cornell.edu/lungdb.html. Date of last visit: 01st January 2017.
Gibbs, P., & Turnbull, L. W. (2003). Textural analysis of contrast-enhanced MR images of the breast. Magnetic Resonance in Medicine, 50(1), 92-98. DOI: 10.1002/mrm.10496.
Guo, Z., Zhang, L., & Zhang, D. (2010). Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern recognition, 43(3), 706-719. DOI: 10.1016/j.patcog.2009.08.017
Haralick, R. M., & Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, (6), 610-621.
Heikkilä, M., Pietikäinen, M., & Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern recognition, 42(3), 425-436.
Hussain, S. U., Napoléon, T., & Jurie, F. (2012). Face recognition using local quantized patterns. In British Machive Vision Conference (pp. 11-pages).
Kalpathy-Cramer, J., de Herrera, A. G. S., Demner-Fushman, D., Antani, S., Bedrick, S., & Müller, H. (2015). Evaluating performance of biomedical image retrieval system- An overview of the medical image retrieval task at ImageCLEF 2004– 2013. Computerized Medical Imaging and Graphics, 39, 55-61. DOI: 10.1016/j.compmedimag.2014.03.004.
Kasturi, R., & Jain, R. (2002). A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video. Pattern recognition, 35(4), 945-965. DOI: 10.1016/S0031-3203(01) 00086-3
Kirk, S., Lee, Y., Roche, C., Bonaccio, E., Filippini, J., & Jarosz, R. (2016). Radiology Data from The Cancer Genome Atlas Thyroid Cancer [TCGA-THCA] collection.The Cancer Imaging Archive. http://doi.org/10.7937/K9/ TCIA.2016. 9ZFRVF1B
Manjunath, K. N., Renuka, A., & Niranjan, U. C. (2007). Linear models of cumulative distribution function for content-based medical image retrieval. Journal of medical systems, 31(6), 433-443. DOI: 10.1007/s10916-007-9075-y.
Müller, H., Kalpathy–Cramer, J., Eggel, I., Bedrick, S., Radhouani, S., Bakke, B & Hersh, W. (2009, September). Overview of the CLEF 2009 medical image retrieval track.In Workshop of the Cross-Language Evaluation Forum for European Languages (pp. 72-84).Springer Berlin Heidelberg. DOI: 10.1007/978-3-642-15751-6_8.
Murala, Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. “Local tetra patterns: a new feature descriptor for content-based image retrieval.” IEEE Transactions on Image Processing 21.5 (2012): 2874-2886. DOI: 10.1109/TIP.2012.2188809. DOI: 10.1109/TIP.2012.2188809
Murala, S., & Wu, Q. J. (2014). Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval. IEEE journal of biomedical and health informatics, 18(3), 929-938. DOI: 10.1109/JBHI.2013.2288522
Murala-2, S., & Wu, Q. J. (2014). MRI and CT image indexing and retrieval using local mesh peak valley edge patterns. Signal processing: image communication, 29(3), 400-409. DOI: 10.1016/j.image.2013.12.002.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern recognition, 29(1), 51-59. DOI: 10.1016/0031-3203(95)00067-4.
Quddus, A., & Basir, O. (2012). Semantic image retrieval in magnetic resonance brain volumes. IEEE transactions on information technology in biomedicine, 16(3), 348-355. DOI: 10.1109/TITB.2012.2189439.
Rao, L. K., & Rao, D. V. (2015). Local quantized extrema patterns for content-based natural and texture image retrieval. Human-centric computing and Information Sciences, 5(1), 1. DOI: 10.1186/s13673-015-0044-z.
Rahman, M. M., Antani, S. K., & Thoma, G. R. (2011). A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Transactions on Information Technology in Biomedicine, 15(4), 640-646. DOI: 10.1109/TITB.2011.2151258.
Scott, G., Shyu, C., R., Knowledge-driven multi-dimensional indexing structure for biomedical media database retrieval. IEEE Trans. Inf. Technol. Biomed.2007; 11 (3): pp. 320-331. DOI: 10.1109/TITB.2006.880551
Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE transactions on image processing, 19(6), 1635-1650. DOI: 10.1109/TIP.2010.2042645.
Unay, D., Ekin, A., & Jasinschi, R. (2008, October). Medical image search and retrieval using local binary patterns and KLT feature points. In 2008 15th IEEE International Conference on Image Processing (pp. 997-1000) IEEE. DOI: 10.1109/ICIP.2008.4711925.
Vipparthi, S. K., & Nagar, S. K. (2015). Directional local ternary patterns for multimedia image indexing and retrieval. International Journal of Signal and Imaging Systems Engineering, 8(3), 137-145. DOI: 10.1504/IJSISE.2015.070485
Vipparthi, S. K., Murala, S., Gonde, A. B., & Wu, Q. J. (2016). Local directional mask maximum edge patterns for image retrieval and face recognition. IET Computer Vision, 10(3), 182-192. DOI:10.1049/iet-cvi.2015.0035.
Vipparthi-2, S. K., Murala, S., Nagar, S. K., & Gonde, A. B. (2015). Local Gabor maximum edge position octal patterns for image retrieval. Neurocomputing, 167, 336-345. DOI: 10.1016/j.neucom.2015.04.062
Vogel, J., & Schiele, B. (2007). Semantic modeling of natural scenes for content-based image retrieval. International Journal of Computer Vision, 72(2), 133-157. DOI: 10.1007/s11263-006-8614-1.
Zhang, L., Zhou, Z., & Li, H. (2012, September). Binary Gabor pattern: An efficient and robust descriptor for texture classification. In 2012 19th IEEE International Conference on Image Processing (pp. 81-84). IEEE.DOI: 10.1109/ICIP.2012.6466 800
Srivastava, V., & Purwar, R. (2018). An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 7(1), 77–89. https://doi.org/10.14201/ADCAIJ2018717789
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