新型非侵入式頸動脈血管疾病檢測方法

New Non-invasive Diagnosis of Carotid Artery Disease

李典儒(Dian-Ru Li)

指導教授 ： 蕭浩明

國立臺灣大學/工學院/機械工程學研究所/碩士(2015年)

https://doi.org/10.6342/NTU.2015.10023

摘要

近年來，頸動脈血管疾病已被證實與高致死率之疾病有相當的關聯性，最常見即為中老年人之腦部中風，造成患者永久傷殘甚至死亡；而頸動脈血管內膜中膜厚度(Intima-media Thickness)也被發現與心血管疾病有正相關性。常見的頸動脈血管疾病檢測方式為頸動脈超音波，利用儀器貼附於受測者頸部肌膚上，藉由超音波探測頸部血管結構，確認血管阻塞狀況，由於其非侵入式的優勢─方便性與安全性，已成為各大醫院篩檢頸部血管健康狀況之有力工具。
然而，其準確性並無血管顯影一般具有高還原性，頸動脈超音波檢檢結果往往與血管顯影之結果有相當的差距，並且須耗費時間與人力完成整個檢測，因此本研究提出一項新型非侵入式頸動脈血管疾病檢測方法，利用影像處理技術來達到醫療檢測之功能，並且只須使用一般相機捕捉之受測者頸部影像即可完成受測者資料採集，整個影像處理流程可在幾分鐘之內完成。本研究首先將影像透過Eulerian影像放大處理凸顯原本肉眼無法輕易觀察到的微小變化，而本研究的放大目標動作即為心跳頻率下頸部的脈搏起伏。處理過後的影像會接著進行量化處理，透過光流法之動作擷取技術，擷取放大過後的動作變化，而後透過主成分分析等數值運算，將頸部影像分為左右兩側分別得出其脈搏動作變化量，並將其變化量簡化為一量化數字指標，藉由一般正常與罹患頸動脈血管疾病之受測者其量化數字指標的不同，將可作為一頸動脈血管健康檢測之診斷標準。本研究可望為未來醫學界提供一新型非侵入式醫療檢測，協助醫療人員診斷受測者頸部血管是否阻塞，更可進一步推測其罹患心血管疾病之風險性，期望可達到早期預防之效果，並給予適當的治療以降低頸動脈血管疾病所造成的高致死率疾病之風險性。

關鍵字

頸動脈血管疾病 ； 非侵入式醫療檢測 ； 影像處理 ； Eulerian影像放大處理 ； 光流法 ； 主成分分析 ； 量化數字指標

並列摘要

Carotid artery disease has been recently confirmed its relation to some highly fatal diseases. For example, cerebral vascular accident (or stroke), which happens commonly in the elders, will cause death or permanent disability. Besides, carotid arterial intima-media thickness has been found to have positive correlation with clinical coronary events. Carotid ultrasonography is a common medical diagnosis of carotid artery disease. The physician will use the ultrasound device with high-frequency sound waves to show the structure of carotid arteries.
However, the accuracy of carotid ultrasonography is not high enough as vascular angiography. Additionally, the whole procedure is time-consuming. In this paper, a new non-invasive diagnosis of carotid artery disease using video processing was investigated. It only required videos of necks of subjects captured by a digital camera and could be accomplished within a few minutes. Eulerian video magnification was used to amplify subtle motions for visualizing the pulse on the human necks. In order to quantify the temporal changes of the magnified video, a quantification model using Optical Flow method was established to capture the magnified variations. The captured variations were then quantified as one numerical criterion through temporal and statistical analysis including PCA (Principal Component Analysis). Results show that the difference of numerical criteria between normal subjects and patients could serve as the guidelines to help future physicians to diagnose carotid artery disease, thereby opening up a wide variety of new treatments and potential applications.

並列關鍵字

Carotid Artery Disease ； Non-invasive Medical Diagnosis ； Video Processing ； Eulerian Video Magnification ； Optical Flow ； Principal Component Analysis ； Quantified Numerical Criterion

參考文獻

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