Abstract
This research aims to work on the specific medical domain. In this work, retrieval of the head–neck medical images from a database is discussed. Content-based medical image retrieval system (CBMIR) is used for retrieving the head–neck images. CBMIR is automatic and more efficient compared with the text-based approach. Shape and texture features are used for constructing feature vector. Texture feature is extracted using a modified Gabor filter based on power-law transformation method. Shape feature is extracted using rank BHMT (rank-order blur hit or miss transformation) method. Shape and texture features are combined to form a single feature vector. Threshold value very near to zero is used to retrieve images from the database. The proposed method is compared with log-Gabor filters and rank BHMT method. Combinations of modified Gabor filter with rank BHMT gave better performance than other methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Gansesan, S., Subashini, T.S.: Classification of medical X-ray images for automated annotation. J. Theor. Appl. Inf. Technol. 63(3), 590–596 (2014)
Akbarpour, Sh: A review on content-based image retrieval in medical diagnosis. Int. J. Tech. Phys. Probl. Eng. (IJTPE) 5(15), 148–153 (2013)
Kumar, M., Singh, M: Content-based medical image retrieval system using DWT and LBP for ear images. I J C T A, International Science Press, 9(40), 353–358 (2016)
Gouid, G.G.N., Nasser, A.A.A.: Automatic identification of head and neck swellings in MRI images using support vector machines based on Cepstral analysis. In: IEEE (2014)
Wicaksono, Y., Wahono, R.S.: Color and texture feature extraction using Gabor filter—local binary patterns for image segmentation with fuzzy C-means. J. Intell. Syst. 1(1), 15–21 (2015)
Rani, R., Kaur, K.: Implementation for Gabor filter using on satellite images enhance the image quality. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(5), 1129–1132 (2013)
Bharath, K., Kurahatti, N.G.: Verilog design for feature extraction using log-Gabor filter for disease detection. Int. J. Technol. Res. Eng. 2(9), 2094–2096 (2015)
Kumar, P.P., Rao, I.K.: Log Gabor filter based feature detection in image verification application. Int. J. Sci. Res. (IJSR) 3(12), 703–707 (2014)
Ramesh Kumar, B., Sivapriya, V.: Diabetes mellitus discovery based on tongue texture features using log Gabor filter mechanism. Int. J. Innov. Res. Comput. Commun. Eng. 3(9), 8671–8676 (2015)
Farajzadeh, M., Mahmoodi, A.: Detection of small target based on morphological filters. In: ICEE (2012)
Pandian, A.A., Balasubramanian, R.: Performance analysis of texture image retrieval for curvelet transform and local ternary pattern using MRI brain tumor. Int. J. Found. Comput. Sci. Technol. (IJFCST) 5(6), 33–46 (2015)
Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. Prentice Hall, Upper Saddle (2012)
Roslan, R., Jamil, N.: Texture feature extraction using 2-D Gabor filters. In: International Symposium on Computer Applications and Industrial Electronics (ISCAIE 2012), December 3–4, Kota Kinabalu Malaysia (2012)
Hammouda, K.: Texture segmentation using Gabor filters. University of Waterloo, Technical Report (2000)
Nava, R., Escalante-Ram’ırez, B., Cristobal, G.: Texture image retrieval based on log-Gabor features. In: CIARP 2012, LNCS 7441, pp. 414–421. Springer, Berlin, Heidelberg (2012)
Malviya, R., Kumar, R., Dangi, A., Kumawat, P.: Verification of palm print using log Gabor filter and comparison with ICA. Int. J. Comput. Appl. Eng. Sci. 1, 222–227 (2011)
Field, D.J.: Relations between the statistics of natural images and the response properties of cortical cells. Opt. Soc. Am. 4(12), 2379–2394 (1987)
Velasco-Forero, S., Angulo, J.: Hit-or-miss transform in multivariate images. In: Blanc-Talon, J. (eds.) ACIVS 2010, Part I, LNCS 6474, pp. 452–463. Springer, Berlin, Heidelberg (2010)
Bloomberg, D.S., Vincent, L.: Pattern matching using the blur hit-miss transform. J. Elect. Imaging 9, 1–22 (2000)
Raghava, Reddy K., Narayana, M.: A comparative study of sift and PCA for content-based image retrieval. Int. Refereed J. Eng. Sci. (IRJES) 5(11), 12–19 (2016)
PEIR digital library. http://peir.path.uab.edu/library/index.php?/category/244
Kumar, M., Singh, M.: Content-based medical image retrieval system using texture and intensity for eye image. Int. J. Sci. Eng. Res. 7(9), 636–640 (2016)
Ahmad, A., Ahmad, S., Hasnat, K.: Fusion of multi-focus images with registration inaccuracies. Signal Image Video Process 11(3), 463–470 (2017)
Ramamurthy, B., Chandran, K.R., Meenakshi, V.R., Shilpa, V.: CBMIR: content based medical image retrieval system using texture and intensity for dental images. In: Mathew, J. et al. (eds.) ICECCS 2012, CCIS 305, pp. 125–134. Springer, Berlin, Heidelberg (2012)
Chandrakar, A., Thoke, A.S., Singh, B.K.: Indexing and retrieval of medical images using CBIR approach. In: CCIS 203, pp. 393–403. Springer, Berlin, Heidelberg (2011)
Kumar, M., singh, kh M.: Content based medical image retrieval system using sift and hu-moment for hepatobiliary images. J. Eng. Appl. Sci. 12(11), 2946–2950 (2017)
Acknowledgements
I would like to thank Dr. Kh. Manglem Singh (Associate Professor), Computer Science and Engineering Department, National Institute of Technology, Manipur, for his support and valuable guidance.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Kumar, M., Singh, K.M. Retrieval of head–neck medical images using Gabor filter based on power-law transformation method and rank BHMT. SIViP 12, 827–833 (2018). https://doi.org/10.1007/s11760-017-1224-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-017-1224-2