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A New Method for Texture Features Extraction (TFE) of Ultra Sound Image Based On Gabor Filter Segmentation Using Matlab

Shiv Kumar, Dr. R.K. Singh, Vijay K Chaudhari, Sushil Kumar, Dr. C.L. Saxena

Abstract


In this paper titled: “A New Method For Texture Features Extraction (TFE) Of Ultra Sound Images Based OnGabber Filter Segmentation Using Matlab” is used for to detect abnormal structural changes of ultra sound images. In research is being conducted with the objective to innovatively develop and apply image segmentation and image feature extraction techniques to efficiently segment the ultrasound image which can be used to automatically detect the diseases in different organs of the body. The process of identifying regions with similar texture and separating regions with different texture is used for the segmentation of ultrasound images.Firstly, a multi-channel texture analysis technique that relies on 2D Gabor Filters is used to isolate regions of perceptually homogeneous texture in an image. Textures are modeled as patterns dominated by a narrow band of spatial frequencies and orientation. Properly tuned Gabor filters react strongly to specific textures and weakly to all others. Then the features of the image were extracted from the image and the clustering of pixels in the feature space produced the segmented image. Unsupervised approach is used for texture segmentation. K-mean clustering method is proposed to cluster the pixels belonging to the same texture region provided the number of different textures in the image is known beforehand.


Keywords


MatLab7.0, Image Processing Toolbox, Biometrics Toolbox, Gabor Filter, Gauss Filter, Clustering, Algorithms

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References


J. Alison Noble, and Djamal Boukerroui,” Ultrasound Image Segmentation: A Survey”, IEEE Transactions on medical imaging,vol. 25, no. 8, August 2006.

Bovik.A.C, Clark M and Geisler W.S, “Multichannel Texture Analysis using Localized spatial filters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.1, pp. 55-73, 1990.

Khaled Hammouda,Prof. Ed Jernigan, “Texture Segmentation Using Gabor Filters “, University of Waterloo, Ontario, Canada, 2001.

FanShao, Keck Voon Ling, Wan Sing Ng and Ruo Yun Wu,” Prostate Boundary Detection from Ultrasonographic Images”, American Institute of Ultrasound in medicine, J Ultrasound Med 22:605-623.0278-4297,2003.

Nualsawat Hiransakolwang, Kien A.Hua, Khanh Vu, Piotr S.Wingyga,“Segmentation of ultrasound liver images: an automatic approach”,multimedia and Expo, 2003, ICME ’03 Proceedings, 2003 international conference.

S.S.Mohamed, E.F.El-Saadany, T.K.Abdel-Galil, J.Shen and M.M.A. Salama, “Region of interest identification in prostate TRUS images based on Gabor Filter”, Micro-Nanomechatronics and human science,2005 IEEE, international symposium 30-30 dec. 2003.

Robert Azencott, Jia-Ping Wang and Laurent Yaenes. “Texture classification using Windowed Fourier Filters”, Analysis and machine intelligence, Vol.19, No.20, February 1997.

K.Mala, V. Sadasivam and S.Alagappan,” Neural network based texture anlysis of liver tumor from Computed Tomography images “,International Journal of bio-medical sciences 2006, vol 2, no. 1.

Jun Xie, Yifing Jiang, Hung-Tat Tsui and Phang-Ann Heng, “boundary Enhancement and speckle reduction for Ultrasound Images via Salient structure Extraction”, IEEE Transactions on Biomedical Engineering,Vol.53, no.11, November 2006.

Gouchol Pok and Jyh-Charn Lice, “Unsupervised texture segmentation based on histogram of encoded Gabor features and MRF model, “IEEE Transactions, 1999”.

Rong Lu , Yi Shen, “Image Segmentation Based on Random Neural Network Model and Gabor Filters “,proceedings of the 2005 IEEE,Engineering in Medicine and Biology 27th Annual Conference, Shanghai, China, Sep.1-4, 2005.

Thomas P. Weldon, William E. Higgm and Dennis F. Denn, “Efficient Gabor Filter Design for texture segmentation”, Preprint of pattern Recognition 1996 Pattern Rec. Soc.

Neeraj Sharma, Amit k. Ray, Shim Sharma, K.K Shukla, Satyajit Pradhan, Lalit M. Aggrawal, Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network, “Journal of Medical Physics,Vol.33, No.3, 2008.

Dinggang Shen, Yiquiang Zhan and Christos Davatzikos, “Segmentation of prostate boundaries from ultrasound images using statistical shape mode”, IEEE transactions on medical imaging,vol. 22, No.4, April 2003.

Jun Xie, Yifeng Jiang, and Hung-tat Tsui, “Segmentation of Kidney From Ultrasound Images Based on Texture and Shape Priors”, IEEE Transactions on medical imaging, vol. 24, no. 1, January 2005.

Jianbo Shi and Jitendra Malik, “Normalized Cuts and image segmentation”, IEEE transaction on pattern analysis and machine intelligence, vol.22, No.8, August 2000.

ZHU Cahng-ming, GU Guo-chang, Liu Hai-bo, SHEN Jing, YU Hualong, “segmentation of ultrasound image based on texture feature and graph cut”, 2008 International conference on computer science and software engineering.


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