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A Study of Automatic Segmentation and Recognition Technology for Common Carotid Artery Ultrasound Spectrum Images
|關鍵字:||頸動脈;common carotid artery;有效波型特徵;超音波頻譜影像;影像辨識;影像切割;effective waveform feature;ultrasound spectrum image;image recognition;image segmentation||出版社:||資訊管理學系所||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. 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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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