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dc.contributorChwei-Shyong Tsaien_US
dc.contributor.authorChen, Chiao-Minen_US
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dc.description.abstract醫學影像切割與辨識技術已廣泛應用於各種疾病之診斷。現今之醫學影像輔助診斷儀器所造影之醫學影像提供的訊息是豐富的,可以保留人工判讀之資訊並且提高診斷的之準確性。隨著老年人口快速劇增,罹患慢性疾病的發病率也日漸增加,其中常見的莫過於心血管之慢性疾病。都卜勒之頸動脈血流超音波頻譜影像是目前診斷心血管疾病之重要工具,由於心血管慢性疾病大多數為頸動脈血管之病變,血管狹窄或是血管阻塞是主要的疾病原因,因此頸動脈之檢查是目前不可或缺的檢驗項目之一。本研究提出一種新的方法,能具體讀取頸動脈都卜勒血流超音波頻譜影像,並自動切割並重建都卜勒之頸動脈血流超音波頻譜影像接著透過超音波影像擷取有效之波形特徵,其有效特徵為五種,分別是: 波型區域面積之比例、水平基準線下之區域面積標籤、水平基準線下之波形面積、波形區域之最高點與波形區域之最低點為五種特徵。本研究方法可自動切割波形區域,傳統上由醫師來區分正常與五種異常之血流型態,在新系統中之五種特徵可診斷血流型態其切割重建之準確率為0.97以及辨識血流型態之準確率為0.97,顯示透過此項技術可幫助醫師正確地並有效診斷血管疾病。zh_TW
dc.description.abstractMedical 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.en_US
dc.description.tableofcontentsAcknowledgement i Abstract in Chinese ii Abstract in English iii Contents v List of Tables vii List of Figures viii Chapter 1 Introduction 1 1.1. Background 1 1.2. Spectrum Image of Ultrasound 2 1.3. Performance of Image Acquisition 4 1.4. Image Segmentation and Recognition 5 1.5. Thesis Organization 8 Chapter 2 Preliminaries 9 2.1. RGB Color Space 9 2.2. Image Binarization 10 2.3. Spatial Filter 12 2.4. Connected- component labeling 15 Chapter 3 The Proposed Scheme 17 3.1. Baseline Detection of Spectrum Images 18 3.2. Waveform Reconstruction 21 3.3. Feature Extraction 25 3.4. Recognition and Analysis of CCA Conditions 28 Chapter 4 Experimental Results 30 4.1. Region Segmentation Evaluation Measures 31 4.2. Recognition and Analysis of The proposed Scheme Evaluation Measures 35 Chapter 5 Conclusions and Future Works 37 5.1. Conclusions 37 5.2. Future Works 37 References 39zh_TW
dc.subjectcommon carotid arteryen_US
dc.subjecteffective waveform featureen_US
dc.subjectultrasound spectrum imageen_US
dc.subjectimage recognitionen_US
dc.subjectimage segmentationen_US
dc.titleA Study of Automatic Segmentation and Recognition Technology for Common Carotid Artery Ultrasound Spectrum Imagesen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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