Abstract
The problem of searching a digital image in a very huge database is called content-based image retrieval (CBIR). Texture represents spatial or statistical repetition in pixel intensity and orientation. When abnormal cells form within the brain is called brain tumor. In this paper, we have developed a texture feature extraction of MRI brain tumor image retrieval. There are two parts, namely feature extraction process and classification. First, the texture features are extracted using techniques like curvelet transform, contourlet transform, and Local Ternary Pattern (LTP). Second, the supervised learning algorithms like Deep Neural Network (DNN) and Extreme Learning Machine (ELM) are used to classify the brain tumor images. The experiment is performed on a collection of 1000 brain tumor images with different modalities and orientations. Experimental results reveal that contourlet transform technique provides better than curvelet transform and local ternary pattern.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rui, Yong, Thomas S. Huang, and Shih-Fu Chang. “Image retrieval: Current techniques, promising directions, and open issues.” Journal of visual communication and image representation 10.1: 39–62, March 1999.
Castelli, Vittorio, and Lawrence D. Bergman, Eds. Image databases: search and retrieval of digital imagery. John Wiley & Sons, 2004.
Castelli, Vittorio, and Lawrence D. Bergman, eds. Image databases: search and retrieval of digital imagery. John Wiley & Sons, 2004.
Murala, Subrahmanyam, and Q. M. Wu. “Local mesh patterns versus local binary patterns: biomedical image indexing and retrieval.” Biomedical and Health Informatics, IEEE Journal of 18.3 (2014): 929–938.
Manjunath, Bangalore S., and Wei-Ying Ma. “Texture features for browsing and retrieval of image data.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 18.8 (1996): 837–842.
Pan, Haiwei, Pengyuan Li, Qing Li, Qilong Han, Xiaoning Feng, and Linlin Gao. “Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph.” Biomedical and Health Informatics, IEEE Journal of 18, no. 2 (2014): 574–584.
Sumana, Ishrat Jahan, Md Monirul Islam, Dengsheng Zhang, and Guojun Lu. “Content based image retrieval using curvelet transform.” In Multimedia Signal Processing, 2008 IEEE 10th Workshop on, pp. 11–16. IEEE, 2008.
Ma, Jianwei, and Gerlind Plonka. “The curvelet transform.” Signal Processing Magazine, IEEE 27.2 (2010): 118–133.
Manju, K., and Smita Tikar. “Contourlet Transform and PNN Based Brain Tumor Classification.” International Journal of Innovative Research and Development (2014).
Javed, Arshad, Wang Yin Chai, Narayanan Kulathuramaiyer, Muhammad Salim Javed, And Abdulhameed Rakan Alenezi. “Automated Segmentation Of Brain Mr Images By Combining Contourlet Transform And K-Means Clustering Techniques.” Journal of Theoretical & Applied Information Technology 54, no. 1 (2013).
Anbarasa Pandian. .A and R.Balasubramainian, “Performance analysis of Texture Image Retrieval in Curvelet Transform, Contourlet Transform and Local Ternary Pattern Using MRI Brain Tumor Images” International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.5, No.6, November 2015.
Murala, Subrahmanyam, R. P. Maheshwari, and R. Balasubramanian. “Local tetra patterns: a new feature descriptor for content-based image retrieval.” Image Processing, IEEE Transactions on 21, no. 5 (2012): 2874–2886.
Hinton, Geoffrey, Li Deng, Dong Yu, George E. Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior et al. “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.” Signal Processing Magazine, IEEE 29, no. 6 (2012): 82–97.
Gladis Pushpa V.P, Rathi and Palani .S, “Brain Tumor Detection and Classification Using Deep Learning Classifier on MRI Images” Research Journal of Applied Sciences Engineering and Technology10(2): 177–187, May-2015, ISSN:20407459.
Huang, Guang-Bin, Qin-Yu Zhu, and Chee-Kheong Siew. “Extreme learning machine: theory and applications.” Neurocomputing 70, no. 1 (2006): 489–501.
Somasundaram .K, S.Vijaylakshmi and T.Kaliselvi, “Segmentation of Brain portion from head scans using k means cluster”, International Journal of Computational Intelligence and Informatics, Vol. 1 : No. 1, April - June 2011.
https://en.wikipedia.org/wiki/Sensitivity and specificity.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media Singapore
About this paper
Cite this paper
Anbarasa Pandian, A., Balasubramanian, R. (2017). Performance Analysis of Texture Image Retrieval in Curvelet, Contourlet, and Local Ternary Pattern Using DNN and ELM Classifiers for MRI Brain Tumor Images. In: Raman, B., Kumar, S., Roy, P., Sen, D. (eds) Proceedings of International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-10-2104-6_22
Download citation
DOI: https://doi.org/10.1007/978-981-10-2104-6_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-2103-9
Online ISBN: 978-981-10-2104-6
eBook Packages: EngineeringEngineering (R0)