CBIR aided classification using extreme learning machine with probabilistic scaling in MRI brain image
-
A. Geetha
and
N. Gomathi
Abstract
An enormous number of magnetic resonance imaging (MRI) brain images were produced in hospitals and several MRI centers. To exploit the diagnosis in MRI brain image, “content-based image retrieval (CBIR)” system is accessed in the MRI brain image database. In this paper, a content-based MRI brain image retrieval system is presented, which is helpful in the medical field to seek a diagnosis in an MRI brain image that is similar to the example given. This paper consists of preprocessing, feature extraction, feature selection, similarity measure, and classification. In the preprocessing phase, the Wiener filter is used to remove the unwanted pixels from an MRI brain image. In the second phase, the features related to MRI brain image are extracted using characteristics of shape, margin, and density of the MRI. In the third stage, the features of MRI brain image were reduced using principal component analysis. CBIR classification is used in this method to gain effectual results. In the first stage, retrieval images are obtained using similarity measures using the similarity between the query image features and the derived trained image features. Finally, the classification stage is an extreme learning machine with probabilistic scaling used to classify the obtained retrieval output image and the query image. The result demonstrates that the proposed CBIR approach is robust and effectual compared with other latest work.
Ethical approval: The conducted research is not related to either human or animal use.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Conflict of interests: The authors declare no conflict of interest.
References
[1] Parizel PM, van den Hauwe L, De Belder F, Van Goethem J, Venstermans C, Salgado R, Voormolen M, Van Hecke W. Magnetic resonance imaging of the brain. Berlin Heidelberg: Springer-Verlag, 2010.10.1007/978-3-540-74504-4_2Search in Google Scholar
[2] Fletcher-Heath LM, Hall DL, Goldgof B, Murtagh FR. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 2001;21:43–63.10.1016/S0933-3657(00)00073-7Search in Google Scholar
[3] Joseph S, John J, Balakrishnan K, Vijayaraghavan PK. Content based image retrieval system for Malayalam handwritten characters. In 2011 3rd International Conference on Electronics Computer Technology, IEEE, Vol. 5; 2011: pp. 386-390.10.1109/ICECTECH.2011.5942026Search in Google Scholar
[4] Zhang W, Dickinson S, Sclaroff S, Feldman J, Dunn S. Shape-based indexing in a medical image database. Biomed Image Anal 1998;221–30.10.1109/BIA.1998.692519Search in Google Scholar
[5] Ramamurthy B, Chandran KR. CBMIR: shape-based image retrieval using Canny edge detection and K-means clustering algorithms for medical images. Int J Eng Sci Technol 2011;3:209–12.Search in Google Scholar
[6] Liao S. Image analysis by moments. PhD thesis, University of Manitoba, Winnipeg, Manitoba, Canada, 1993.Search in Google Scholar
[7] Ghosal S, Mehrotra R. Edge detection using orthogonal moment-based operators. In: Proceedings of 11th Image, Speech and Signal Analysis (IAPR) International Conference on Pattern Recognition, vol. III, 1992, pp. 413–6.Search in Google Scholar
[8] Parades R, Keysers D, Lehman TM, Wein B, Ney H, Vidal E. Classification of medical images using local representation. In: Workshop Bildverarbeitung fur die Medizin, 2002, pp. 171–4.10.1007/978-3-642-55983-9_39Search in Google Scholar
[9] Nazari MR, Fatemizadeh E. A CBIR system for human brain magnetic resonance image indexing. Int J Comput Appl 2010;7:33–37.10.5120/1327-1636Search in Google Scholar
[10] Habashy SM. Content-based image retrieval (CBIR) system aided tumor detection. Int J Comput Sci Eng Inform Technol Res 2013;3:329–40.Search in Google Scholar
[11] Oberoi A, Singh M. Content based image retrieval system for medical databases (CBIR-MD) – lucratively tested on endoscopy, dental and skull images. Int J Comput Sci Iss 2012;9:300.Search in Google Scholar
[12] Srilakshmi G, Reddy KR. Performance enhancement of content based medical image retrieval for MRI brain images based on hybrid approach. Int Res J Eng Technol 2015;2.Search in Google Scholar
[13] Boykov Y, Veksler O, Zabih R. Markov random fields with efficient approximations. In: IEEE Comput Soc Conf 1998;648–55.10.1109/CVPR.1998.698673Search in Google Scholar
[14] Besag J. On the statistical analysis of dirty pictures. J Roy Stat Soc B Met 1986;48:259–302.10.1111/j.2517-6161.1986.tb01412.xSearch in Google Scholar
[15] Wang L, Healey G. Using Zernike moments for the illumination and geometry invariant classification of multispectral texture. IEEE Trans Image Process 1998;7:196–203.10.1109/83.660996Search in Google Scholar PubMed
[16] Ghosal S, Mehrotra R. Segmentation of range images: an orthogonal moment-based integrated approach. IEEE Trans Robot Autom 1993;9:385–99.10.1109/70.246050Search in Google Scholar
[17] Iskander DR, Collins MJ, Davis B. Optimal modeling of corneal surfaces with Zernike polynomials. IEEE Trans Biomed Eng 2001;48:87–95.10.1109/10.900255Search in Google Scholar PubMed
[18] Iskander DR, Morelande MR, Collins MJ, Davis B. Modelling of corneal surfaces with radial polynomials. IEEE Trans Biomed Eng 2002;49:320–8.10.1109/10.991159Search in Google Scholar PubMed
[19] Xin Y, Liao S, Pawlak M. Geometrically robust image watermarking via pseudo-Zernike moments. In: Proceedings of 2004 Canadian Conference on Electrical and Computer Engineering, vol. 2, 2004, pp. 939–42.Search in Google Scholar
[20] Haddadnia J, Ahmadi M, Raahemifar K. An effective feature extraction method for face recognition. In: Proceedings of 2003 International Conference on Image Processing, vol. 3, 2003, pp. III– 917–20.10.1109/ICIP.2003.1247395Search in Google Scholar
[21] Haddadnia J, Ahmadi M, Faez K. A feature extraction method with pseudo-Zernike moments in RBF neural network-based human face recognition system. EURASIP J Appl Signal Process 2003;890–901.10.1155/S1110865703305128Search in Google Scholar
[22] Kim Y-S, Kim W-Y. Content-based trademark retrieval system using visually salient features. In: Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997, pp. 307–12.Search in Google Scholar
[23] Pang Y-H, Teoh AB, Ngo DC, Hiew F-S. Palmprint verification with moments. J WSCG 2003;12:1–3.Search in Google Scholar
[24] Zhang H, Zhu Q, Fan C, Deng D. Image quality assessment based on Prewitt magnitude. Int J Electron Commun 2013;67:799–803.10.1016/j.aeue.2013.04.001Search in Google Scholar
[25] Pearson K. On lines and planes of closest fit to systems of points in space. Philos Mag 1901;2:559–72.10.1080/14786440109462720Search in Google Scholar
[26] Jolliffe IT. Principal component analysis, 2nd ed. New York: Springer, 2002.Search in Google Scholar
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Articles in the same Issue
- Research Articles
- CBIR aided classification using extreme learning machine with probabilistic scaling in MRI brain image
- Feature selection for classification in Steady state visually evoked potentials (SSVEP)-based brain-computer interfaces with genetic algorithm
- Modelling effects of consciousness disorders in brainstem computational model – Preliminary findings
- The use of modern IT solutions for educational purposes on the example of an application enabling to visualize the human eye
- Review
- Technical infrastructure for curriculum mapping in medical education: a narrative review
- Short Communication
- Protein folding vs. COVID-19 and the Mediterranean diet
