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Attribute based Level Adaptive Thresholding Algorithm for Object Extraction

Ankush Rai


Image processing plays a vital role in the computer vision because most of the scenarios require object extraction and recognition. In order to utilize these images for the procedural analysis of the environmental entities in computational means, it must be a noiseless one. However, the images are affected through noises caused by the various acquisition techniques and hence an effective technique for denoising and segmentation is necessary in Computational Tomography. In order to achieve this objective we propose an effective denoising technique which confiscates the advantages offered in thresholding methods.

Keywords: Computer Vision, Image Segmentation, Image denoising

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YangWang, Haomin Zhou, Total Variation Wavelet-Based Medical Image Denoising, Int. J. Biomed. Imag. January 2006; 2006: 1–6p.

Ahmed Badawi, Scatterer Density in Nonlinear Diffusion for Speckle Reduction in Ultrasound Imaging: The Isotropic Case, Int. J. Biolog. Life Sci. 2006; 2(3): 149–167p.

Sudha, Suresh, Sukanesh, Comparative Study on Speckle Noise Suppression Techniques for Ultrasound Images, Int. J. Eng. Technol. April 2009; 1(1): 57–62p.

Shujun Fu, Qiuqi Ruan, Wenqia Wang, et al. Feature Preserving Nonlinear Diffusion for Ultrasonic Image Denoising and Edge Enhancement, World Acad. Sci. Eng. Technol. February 2005; 2: 148–151p.

Shujun Fu, Qiuqi Ruan, Wenqia Wang, et al. Adaptive Anisotropic Diffusion for Ultrasonic Image Denoising and Edge Enhancement, Int. J. Inform. Technol. 2006; 2(4): 284–287p.

Tanaphol Thaipanich, Jay Kuo, An Adaptive Nonlocal Means Scheme for

Medical Image Denoising, In Proceedings of SPIE Medical Imaging, San Diego, CA, USA, 7623: February 2010.

Su Cheol Kang, Seung Hong Hong, A Speckle Reduction Filter using Wavelet- Based Methods for Medical Imaging Application, In Proceedings of 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, October 2001; 3: 2480–2483p.

Jose V. Manjón, Neil A. Thacker, Juan J. Lull, et al. Multicomponent MR Image Denoising, J. Biomed. Imaging, 2009; 2009(18): 1–27p.

Hyder Ali, Sukanesh and Fellow, An Edge Preserving Denoising Technique for MR Images using Curvelet Transform, Interdisciplinarty J. May 2010; 91: 3–8p.

Fernanda Palhano Xavier de Fontes, Guillermo Andrade Barroso, Pierre Hellier, Real Time Ultrasound Image Denoising, J. Real-Time Image Process. April 2010; 1:1–14p.

Perona, Malik, Scale-space and Edge Detection using Anisotropic Diffusion, IEEE T. Pattern Anal. 1990; 12(7): 629–639p.

Manjon, Robles, Thacker, Multispectral MRI de-noising using Non-local Means, In Proceedings of MIUA, Aberystwyth, 2007, 41–46p.

Gerig, Kubler, Kikinis, et al. Nonlinear Anisotropic Filtering of MRI Data, IEEE T. Med. Imaging, 1992; 11(1): 221–232p.

Wood, Johnson, Wavelet Packet Denoising of Magnetic Resonance Images: Importance of Rician Noise at Low SNR, Magn. Reson. Med. 1999; 41(1): 631–635p.

Dar-Ren Chen, Ruey-Feng Chang, Wen-Jie Wu, et al. 3-D Breast Ultrasound Segmentation Using Active Contour Model, Ultrasound Med. Biol. 2003; 29(7): 1017–1026p.

Buades, Coll, Morel, A Review of Image Denoising Algorithms, With a New One, SIAM J. Multiscale Model. Simulat. 2005; 4(2): 490–530p.


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