Ultrasound applications are used for diagnostic applications such as visualizing muscles, tendons, internal organs, to determine its size, structures, any lesions or other abnormalities. This paper concentrates the diagnosis of abnormalities in kidney Images based on retrieving past similar images from kidney Image Database. More and more amount of ultrasound digital images are being captured and stored in clinical laboratories. In order to use this information, a time efficient retrieval technique is required. One major development in this area is content based image retrieval(CBIR) . The CBIR techniques use image features for image indexing and retrieval. The main features used for image retrieval are color, texture and shape. This Paper looks into the image retrieval technique based on texture, because of same modality ultrasound kidney images. The Most familiar Texture feature extraction technique is using the Two Dimensional Gray level Co-occurrence Matrix (2D-GLCM). But the problem with this method is Computational overhead. To overcome this difficulty, this paper experiments the texture feature extraction by Computationally efficient Gray level Co-occurrence Vector(GLCV), which is called one dimensional Gray level Co-occurrence Matrix(1D-GLCM). The 1D-GLCM Texture feature representation is the central theme of this proposed work and the Performance the system based on 1D-GLCM is compared with traditional two dimensional GLCM called 2D-GLCM. Experimental results show that this technique achieves higher Recall rates with the lesser time compared with traditional 2D-GLCM.

1. . S. Manikandan and Dr. V. Rajamani, " Automated Feature Extraction and Retrieval of Ultrasound Kidney Images using Maximin Approach", International Journal of Computer Applications(0975-8887),Volume 4 –No1,July 2010,PP 42-46
2. . Y. Rui and T. S. Huang, "Image retrieval: Current techniques, promising directions and open issues," J. Vis. Commun. Image Represent. , vol. 10, no. 4, pp. 39–62, Apr. 1999.
3. . A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, "Content-based image retrieval at the end of the early years," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 22, no. 12, pp. 1349–1380, Dec. 2000.
4. . . M. Kokare, B. N. Chatterji, and P. K. Biswas, "A survey on current content based image retrieval methods," IETE J. Res. , vol. 48, no. 3/4,pp. 261–271, May–Aug. 2002.
5. . H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler, "A review of content-based image retrieval systems in medical applications—clinical benefits and future directions," Int. J. Med. Inform. , vol. 73, no. 1, pp. 1– 23, 2004.
6. . K. Thangavel , R. Manavalan, I. Laurence Aroquiaraj," Removal of Speckle Noise from Ultrasound Medical Image based on Special Filters: Comparative Study", ICGST-GVIP Journal, ISSN 1687-398X, Volume (9), Issue (III), June 2009, pp 25-32.
7. . C. Callins Christiyana,Dr. J. Sutha and M. Sheerin Banu," Shape Based Image Retrieval using Cellular Automata and Fourier Descriptors",IEEE International Advanced Computing Coference (IACC) 2009.
8. . Jing Yi Tou, Yong Haur Tay, Phooi Yee Lau ," One-dimensional Grey-level Co-occurrence Matrices for texture classification", IEEE International symposium on Information Technology 2008, pp 1-6.
9. . M. Petrou, and P. G. Sevilla, "Image processing: Dealing with texture", Wiley, 2006.
10. . R. M. Haralick, K. Shanmugam, and I. Dinstein, "Textural features for image classification", IEEE Transcations on Systems, Man, and Cybernetics, 1973, pp. 610-621.
11. . J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever. "Mutual-information-based registration of medical images: A survey[J]", IEEE Trans on Medical Imaging, 2003
12. . A. Laine and J. Fan, "Texture classification by waelet packet signature," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 15, no. 11, pp. 1186– 1191, Nov. 1993.
13. T. Chang and C. C. J. Kuo, "Texture analysis and classification with treestructured wavelet transform," IEEE Trans. Image Process. , vol. 2, no. 4 pp. 429–441, Apr. 1993.
14. C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Image segmentation using expectation maximization and its applications to image querying. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 24(8):1026- 1038.
15. van Leeuwen, Jan, ed. (1990), Handbook of theoretical computer science (vol. A): algorithms and complexity, MIT Press, ISBN 978-0-444-88071-0

Ultra Sound Kidney Image Cbir Texture 1d-glcm 2d-glcm Recall