Abstract
Due to the vast number of medical technologies and equipments, the medical images are growing at a rapid rate. This directs to retrieve efficient medical images based on visual contents. This paper proposed the magnetic resonance imagining (MRI) scan image retrieval system using co-occurrence matrix-based texture features. Here, the principal component analysis (PCA) is applied for optimized feature selection to overcome the difficulties of feature vector creation with Haralick’s texture features. Then, K-means clustering and Euclidean distance measure are used to retrieve best MRI scan images for the query image in medical diagnosis. The experimental results demonstrate the efficiency of this system in clusters accuracy and best MRI scan image retrieval against using all the fourteen familiar Haralick’s texture features.
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
H. Muller, N. Michoux, D. Bandon, A. Geissbuhler, A review of content-based image retrieval systems in medical applications: clinical benefits and future directions. Int. J. Med. Inform. 73, 1–23 (2004)
X.S. Zhou, S. Zillner, M. Moeller et al., Semantics and CBIR: a medical imaging perspective, in ACM Conference on Content-based Image and Video Retrieval (2008) pp. 571–580
C.B. Akgül, D.L. Rubin, S. Napel, C.F. Beaulieu et al., Content-based image retrieval in radiology: current status and future directions. J. Dig. Imag. 24(2), 208–222 (2011)
B. Ramamurthy, K.R. Chandran, Content based medical image retrieval with texture content using gray level co-occurrence matrix and K-means clustering algorithms. J. Comput. Sci. 8(7), 1070–1076 (2012)
U. Sinha, H. Kangarloo, Principal component analysis for content-based image retrieval. Radiographics 22, 1271–1289 (2002)
G.N. Lee, H. Fujita, K-means clustering for classifying unlabelled MRI data, in IEEE Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (2007) pp. 92–98
S. Ayyachamy, V.S. Manivannan, Distance measures for medical image retrieval. Int. J. Imag. Syst. Technol. 23(1), 9–21 (2013)
R. Haralick, K. Shanmugam, I. Dinstein, Textural features for image classification. IEEE Trans. Syst. Man. Cybern. 3(6), 610–621 (1973)
H. Shao, W.C. Cui, H. Zhao, Medical image retrieval based on visual contents and text information, in IEEE International Conference on Systems (2004) pp. 1098–1103
A.G. Selvarani, S. Annadurai, Medical image retrieval by combining low level features and Dicom features, in IEEE International Conference on Computational Intelligent and Multimedia Applications (2007), pp. 587–589
W. Horsthemke, D. Raicu, J. Furst, Task-oriented medical image retrieval, in MICCAI Work Shop Proceedings (2007) pp. 31–44
P. Zhang, H. Zhu, Medical image retrieval based on co-occurrence matrix and edge histogram, in IEEE conference on multimedia technology (2011) pp. 5434–5437
T.R. Sivapriya, V. Saravanan, P. Ranjit Jeba Thangaiah, Texture analysis of brain MRI and classification with BPN for the diagnosis of dementia. Eng. Inf. Technol. Commun. Comput. Inf. Sci. 20(4), 553–563 (2011)
B.G. Prasad, A.N. Krishna, Statistical texture feature-based retrieval and performance evaluation of CT brain images, in IEEE International Conference on Electronics Computer Technology (2011) pp. 1–4
N. Kumaran, R. Bhavani, Spine MRI image retrieval using texture features. Int. J. Comput. Appl. 46(24), 1–7 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Kumaran, N., Bhavani, R. (2015). PCA-Based Feature Selection for MRI Image Retrieval System Using Texture Features. In: Suresh, L., Dash, S., Panigrahi, B. (eds) Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 324. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2126-5_13
Download citation
DOI: https://doi.org/10.1007/978-81-322-2126-5_13
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2125-8
Online ISBN: 978-81-322-2126-5
eBook Packages: EngineeringEngineering (R0)