The goal of the proposed system is to retrieve the corresponding image from the database based on the query image. Now-a-days images are stored in the database in the form of digital. Thus, retrieval of image from huge database become complex. Most of the existing system uses indirect method of retrieval and they have no methodology. Thus the major aim of our approach is to construct an effective and efficient search engine tool in order to retrieve image from a huge database based on the user query.

1. Haralick, R. M. , K. Shanmugam and I. Dinstein, 1973. Textural features for image classification. IEEE Trans. Syst. Man Cybermetics, 3: 610-621. DOI: 10. 1109/TSMC. 1973. 4309314
2. Shyu, C. R. , C. E. , Brodely, A. C. Kak, A. Kosaka and A. M. Aisen et al,. 1999. ASSERT: A physician-in-the-loop content-based retrieval system for HRCT image databases. Comput. Vis. Image Understand. ,75: 111-132. DOI: 10. 1006/cviu. 1999. 0768
3. Thies, C. , M. O. Guld, B. Fischer and T. M. Lehmann, 2005. Content-based queries on the CasImage database within the IRMA framework. Lecture Notes Comput. Sci. , 3491: 781-792.
4. Sohail, A. S. M. , P. Bhattacharaya, S. P. Mudur, S. Krishnamurthy and L. Gilbert,2010. Content-based retrieval and classification of ultrasound medical images of ovarian cysts. Artif. Neur. Netw. Patt. Recog. , 5998: 173-184. DOI: 10. 1007/978-3-642-12159-3_16.
5. Thoma, G. R. ,L. R. Long and S. Antani, 2006. Biomedical Imaging research and development: Knowledge from images in the medical enterprise. U. S. National Library of Medicine.
6. B. Ramamurthy, K. R. Chandran, S. Aishwarya, P. Janaranjani, 2011. CBMIR: Content Based Image Retrieval using Invariant Moments, GLCM and Grayscale Resolution for Medical Images, European Journal of Scientific Research ISSN 1450-216X Vol. 59 No. 4 pp. 460-471.
7. Manavalan Radhakrishnan and Thangavel Kuttiannan, Comparative Analysis of Feature Extraction Methods for the Classification of Prostate Cancer from TRUS Medical Images, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 2, January 2012
8. Bino Sebastian V, A. Unnikrishnan and Kannan Balakrishnan,Grey Level Co-occurrences Matrices:Generalization and its Features, International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 2, No. 2, April 2012
9. Anal Kumar Mitra, DR. Ranjan Parekh, 2011. Automated Detection of skin diseases using texture features, International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462.
10. Mari Partio, Bogdan Cramariuc, Moncef Gabbouj, and Ari Visa,Rock Texture Retrieval using Grey Level Co-occurrence Matrix.

Ifex- Image Feature Extraction Gray Level Co-occurrence Matrix K-means Clustering Algorithm Texture Image Retrieval Precision Recall