Research of Computer-Aided Diagnosis about Pulmonary Interstitial Pathology Based on Wavelet Decomposition

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This paper present a way of pulmonary interstitial pathology diagnosis of computer-aided diagnosis based on wavelet analysis. It is difficult to diagnosis qualitatively in pulmonary interstitial pathology because various lesions of the image analogous and the image interlap. The method based on good time-frequency characters is put. The effectiveness and accuracy of the means is verified through the simulation experiment of denoised image, image segmentation and image characteristics extraction. Along with the further research and application of the wavelet technique, it will have more space that use wavelet analysis in computer-aided diagnosis.

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611-614

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February 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Wolfgang Wein, Shell by Brunke, Ali Khanmene. Automatic CT-ultrasound registration for diagnostic imaging and image-guided interven- tion[J]. Medical Image Analysis, 2008, 12: 577-585.

DOI: 10.1016/j.media.2008.06.006

Google Scholar

[2] Eslami.R. and Radha.H., Regular hybrid wavelets and directional filter banks: extensions and applications[C]. IEEE International Conference on Image Processing, 2006: 1609-1612.

DOI: 10.1109/icip.2006.312617

Google Scholar

[3] Wyawahare M V, Patil P M, Abhyankar H K. Image Registration Techniques: An overview[J]. International Journal of Signal Processing, Image Processing and Pattern Recognition, 2009, 2(3)11-28.

Google Scholar

[4] Jamshid D, Hamdan A, Manlio V, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach [J]. IEEE Trans on Med Imaging, 2008, 27(4): 467-480.

DOI: 10.1109/tmi.2007.907555

Google Scholar

[5] Ramin Eslami, Hayder Radha. A new family of nonredundant transforms using hybrid wavelets and directional filter banks [J]. In IEEE Transactions on image processing, 2007, 16(4): 1152-1167.

DOI: 10.1109/tip.2007.891791

Google Scholar

[6] A. Rav-Acha,Y. Pritch,D. Lischinski, et, al. Dynam-osaicing: Mosaicing of Dynamic Scenes, IEEE Trans. PAMI, Oct. 2007: 1789-1801.

DOI: 10.1109/tpami.2007.1091

Google Scholar

[7] Shuqian Luo and Guohong Zhou, Medical Image Processing and Analyse. [M]. Publishing House of Science in Beijing, (2010).

Google Scholar

[8] VandenElsen P A, Pol E J D, Vierge M A. Medical Image matching: A review with classification[J]. IEEE Eng. Med. Biol., 2009, I2(3): 12-39.

Google Scholar

[9] Kenneth R. Castleman. Digital Image Processing. [M]. Publishing House of Electronics Industry in Beijing, (2002).

Google Scholar

[10] Nie sheng-dong, Li Lihong, Chen Zhaoxue. A XI feature-based pulmonary nodule segmentation using three-domain mean shift clustering [J]. Journal of East China Normal University. 2008(1): 60-67.

DOI: 10.1109/icwapr.2007.4420699

Google Scholar