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A Study of Automatic Segmentation and Recognition Technology for Common Carotid Artery Ultrasound Spectrum Images
common carotid artery
effective waveform feature
ultrasound spectrum image
|引用:|| M. S. Atkins and B. T. Mackiewich, “Fully automatic segmentation of the brain in MRI,” IEEE Transactions on Medical Imageing, Vol. 17, No. 1, 1998, pp. 98-107.  S. Arora, J. Acharya, A. Verma and P. K. Panigrahi, “Multilevel thresholding for image segmentation through a fast statistical recursive algorithm,” Pattern Recognition Letters, Vol. 29, No. 2, 2008, pp. 119-125.  Anzalone, F. Bizzarri, M. Parodi and M. Storace, “A modular supervised algorithm for vessel segmentation in red-free retinal images,” Computers in Biology and Medicine, Vol. 38, No. 8, 2008, pp. 913-922.  J. Babaud, A. P. Witkin, M. Baudin and R. 0. Duda, “Uniqueness of the Gaussian kernel for scale-space filtering,” IEEE Transactions on Pattern Analysis and Machine and Machine Intelligence, Vol. 8, No. 1, 1986, pp. 26-33.  H. D. Cheng, X. H. Jiang, Y. Sun and J. Wang, “Color image segmentation: advances and prospects,” Pattern Recognition, Vol. 34, No. 12, 2001, pp. 2259-2281.  J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine and Machine Intelligence, Vol. 8, No. 6, 1986, pp. 679-698.  F. Dirgenali and S. Kara, “Recognition of early phase of atherosclerosis using principles component analysis and artificial neural networks from carotid artery Doppler signals,” Expert Systems with Applications, Vol. 31, No. 3, 2006, pp. 643-651.  D. Y. Huang and C. H. Wang, “Optimal multi-level thresholding using a two-stage Otsu optimization approach,” Pattern Recognition Letters, Vol. 30, No. 3, 2009, pp. 275-284.  L. He, Y. Chao, K. Suzukic and K. Wu, “Fast connected-component labeling,” Pattern Recognition, Vol. 42, No. 9, 2009, pp. 1977-1987.  G. W. Jiji, “Colour texture classification for human tissue images”, Applied Soft Computing, Vol. 11, No. 2, 2011, pp. 1623-1630.  J. Jing, W. Yan, G. Xin, S. Yi and W. Qi, “Automatic measurement of the artery intima-media thickness with image empirical mode decomposition,” in Proceedings of the Imaging Systems and Techniques, 2010, Thessaloniki.  S. Kucheryavski, “Using hard and soft models for classification of medical images,” Chemometrics and Intelligent Laboratory Systems, Vol. 88, No. 1, 2007, pp. 100-106.  H. Lai, S. S. Yu, H. Y. Tseng and M. H. Tsai, “A protozoan parasite extraction scheme for digital microscopic images,” Computerized Medical Imaging and Graphics, Vol. 34, No. 2, 2010, pp. 122-130.  X. Li and T. Chen, “Efficient synthesis of parameterized Gaussian-like filters by approximation,” Signal Processing, Vol. 41, No. 2, 1995, pp. 119-134.  F. Molinari, G. Zeng and J. S. Suri, “A state of the art review on intima–media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound,” Compute Methods Programs Biomed, Vol.100, No. 3, 2010, pp. 201-221.  F. Molinari, G. Zeng and J. S. Suri, “Intima-Media Thickness: Setting a Standardfor a Completely Automated Method of Ultrasound Measurement,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, Vol. 57, No. 5, 2010, pp. 1112-1124.  M. Mignotte, “Segmentation by fusion of histogram-based k-means clusters in different color spaces,” IEEE Transactions on Image Processing, Vol. 17, No. 5, 2008, pp. 780-787.  H. P. Ng, S. H. Ong, K. W. C. Foong, P. S. Gohf and W. L. Nowinski, “Masseter segmentation using an improvedwatershed algorithm with unsupervised classification,” Computers in Biology and Medicine, Vol. 38, No. 2, 2008, pp. 171-184.  N. Otsu, “A threshold selection method from Gray-Level histograms,” IEEE Transactions on Systems and Cybernetics, Vol. 9, No. 1, 1979, pp. 62-66.  D. Pascale, “A Review of RGB color spaces,” http://www.babelcolor.com/download/A%20review%20of%20RGB%20color%20spaces.pdf, 2007, pp. 2-4.  J. K. Park, S. H. Kim, B. S. Kim and G. Choi, “Two cases of aberrant right subclavian artery and right vertebral artery that originated from the right common carotid artery,” Korean J Radiol, Vol. 9, 2008, pp. 39-42.  W. C. A. Pereira, A. V. Alvarenga, A. F. C. Infantosi, L. Macrini and C. E. Pedreira, “A non-linear morphometric feature selection approach for breast tumor contour from ultrasonic images,” Computers in Biology and Medicine, Vol. 40, No. 11, 2010, pp. 912-918.  E. L. Rubio, “Restoration of images corrupted by Gaussian and uniform impulsive noise,” Pattern Recognition, Vol. 43, No. 5, 2010, pp. 1835-1846.  R. Rocha, A. Campilho, J. Silva, E. Azevedo and R. Santos, “Segmentation of the carotid intima-media region in B-mode ultrasound images,” Image and Vision Computing, Vol. 28, No. 4, pp. 614-625.  F. D. Santos, M. A. Gutierrez and E. T. Costa, “Determination of the variation of the intima-media thickness and the diameter of arteries from echocardiographic ultrasound image sequences,” Physics Procedia, Vol. 3, No. 1, 2010, pp. 407-413.  K. Somkantha and N. T. Umpon, “Boundary detection in medical images using edge following algorithm based on intensity gradient and texture gradient features,” IEEE Transactions on Biomedical Engineering, Vol. 58, No. 3, pp. 567-573.  K. Suzuki, I. Horiba and N. Sugie, “Linear-time connected-component labeling based on sequential local operations,” Computer Vision and Image Understanding, Vol. 89, No. 1, 2003, pp.1-23.  S. Tsantisa, N. Dimitropoulosc, D. Cavourasb and G. Nikiforidisa, “Morphological and wavelet features towards sonographic thyroid nodules evaluation,” Computerized Medical Imaging and Graphics, Vol. 33, No. 2, 2009, pp. 91-99.  Yılmaz and N. F. Guler, “Analysis of the doppler signals using largest lyapunov exponent and correlation dimension in healthy and stenosed internal carotid artery patients,” Digital Signal Processing, Vol. 20, No. 2, 2010, pp. 401-409.  L. Wu and Z. Xie, “Scaling theorems for Zero-Crossings,” IEEE Transactions on Pattern Analysis and Machine and Machine Intelligence, Vol. 12, No. 1, 1990, pp. 46-54.  Z. Wei, J. Wang, H. Nichol, S. Wiebe and D. Chapman, “A median-gaussian filtering framework for moire pattern noise removal from X-ray microscopy image,” Micron, Vol. 43, No. 2-3, 2012, pp. 170-176.  B. Zhang, L. Zhang, L. Zhang and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of Gaussian,” Computers in Biology and Medicine, Vol. 40, No. 4, 2010, pp. 438-445.|
Medical image segmentation and recognition technology has been widely used in the diagnosis of various diseases. The information in medical images from modern instruments is rich. With the application of image recognition technology, manual reading time can be saved and diagnostic accuracy can be enhanced.With rapid expansion of the population of elderly people, the incidence of chronic diseases also increased. The most common among them is no doubt the cardiovascular diseases. The Common Carotid Artery (CCA) ultrasound spectrum image is an important tool in diagnosis of cardiovascular diseases. As vascular stenosis or occlusions are the main pathological change of vascular disorders, carotid artery examination is essential in the diagnosis of cardiovascular diseases.This thesis proposes a new method specific to the reading of ultrasound spectrum images of common carotid artery blood flow. The proposed scheme automatically segments reconstruction the waveform image and extracts effective waveform features from the images for diagnostic purposes by using the following five criteria: the ratio of the waveform region, the waveform region area target under the horizontal baseline, the waveform region area under the horizontal baseline, the highest point of the waveform region, and the lowest point of the waveform region.The proposed scheme can automatically segment the waveform region. Traditionally used by physicians to differentiate between normal blood-flow patterns and five abnormal blood-flow types, these five criteria are now employed by the new system to digitally diagnose vascular disorders at an accuracy rate as high as 0.97 and a segments reconstruction accuracy rate as high as 0.97. With this technology, physicians can diagnose vascular disorders correctly and efficiently.
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