基于內(nèi)容和時空特征的面癱程度定量評價的鑒別研究
發(fā)布時間：2018-07-27 17:00
【摘要】：面部麻痹(面癱)是一種常見的臨床疾病,據(jù)統(tǒng)計每10萬人中就有20至25名患者。此疾病通常由神經(jīng)損傷導致,如早期沒到得到正確的治療,可能會留下嚴重的后遺癥。如有一種能有效地客觀定量評價患者面部麻痹程度的方法,對治療是非常有幫助的。針對這種需求,本文應用面部表情數(shù)據(jù)提取更精確特征信息,為醫(yī)生更客觀地鑒別面癱患者的面癱程度提供指導,主要研究成果如下:(1)針對傳統(tǒng)研究方法對靜態(tài)圖像因定位、光照、陰影和本身造成不對稱性的弊端,基于圖像的內(nèi)容一方面提出了基于LBP和Gabor濾波器相結(jié)合的方法(LBP+Gabor)提取不對稱特征和運動特征。利用LBP旋轉(zhuǎn)不變性和灰度不變性的優(yōu)點去除光照影響;再利用Gabor濾波器減少LBP處理后圖像中的噪聲和冗余信息。另一方面,針對Gabor濾波器中通帶的不均勻,提出了基于CMF(Concentric Modulation Filter)方法的特征提取,并將LBP和CMF結(jié)合(LBP+CMF)。由于CMF的通帶對所有方向都是相同的,能提取更精確的特征。實驗表明,LBP+Gabor和LBP+CMF方法相對于傳統(tǒng)方法具有較好的有效性和實用性。(2)靜態(tài)圖像中的不相關(guān)區(qū)域(如嘴巴里的牙齒、陰影和嘴角的痣)影響了特征提取的精確度。為了解決這些問題,本文根據(jù)面部數(shù)據(jù)的數(shù)據(jù)特征信息,首次提出了應用目標跟蹤方法提取時空特征。此方法首先從面部圖像第一幀中選取marker,然后在圖像序列中追蹤marker提取時空特征。本文應用KLT和Mean-Shift兩種跟蹤方法證明了提取時空特征對鑒別有效性。(3)針對不同患者表情執(zhí)行的不同步問題,本文對表情的開始時間點和結(jié)束時間點進行同步和速度歸一化。多類數(shù)據(jù)實驗表明,提取時空特征,并進行同步和歸一化后,相比基于內(nèi)容的方法預測面癱倒的程度正確率更高,對醫(yī)生具有更好的實用性。同時,本文提出的方法拓展了目標跟蹤方法的應用領(lǐng)域,也為研究面癱程度定量評估提供了新思路。實驗結(jié)論有助于醫(yī)生對患者做出合理的病情判斷和治療。
[Abstract]:Facial paralysis (facial paralysis) is a common clinical disease, according to statistics every 100000 people with 20 to 25 patients. The disease is usually caused by nerve damage, which can lead to severe sequelae if not properly treated at an early stage. If there is an effective method to evaluate the degree of facial paralysis objectively and quantitatively, it is very helpful for the treatment. In response to this demand, this paper uses facial expression data to extract more accurate feature information, and provides guidance for doctors to identify the degree of facial paralysis more objectively. The main research results are as follows: (1) aiming at the traditional research methods to locate the static image, Illumination, shadow and itself cause asymmetry. On the one hand, based on image content, a method based on LBP and Gabor filter is proposed to extract asymmetric features and motion features by (LBP Gabor). The advantages of LBP rotation invariance and gray invariance are used to remove the illumination effect, and the Gabor filter is used to reduce the noise and redundant information in the image processed by LBP. On the other hand, aiming at the nonuniformity of passband in Gabor filter, the feature extraction based on CMF (Concentric Modulation Filter) method is proposed, and LBP and CMF are combined with (LBP CMF). Because the passband of CMF is the same in all directions, it can extract more accurate features. The experimental results show that the LBP Gabor and LBP CMF methods are more effective and practical than the traditional methods. (2) the accuracy of feature extraction is affected by the uncorrelated regions in the still images (such as teeth in the mouth, shadows and nevus in the corners of the mouth). In order to solve these problems, based on the feature information of facial data, a target tracking method is proposed to extract temporal and spatial features for the first time. The method firstly selects Marker from the first frame of facial image, and then tracks the marker to extract temporal and spatial features in the image sequence. In this paper, two tracking methods, KLT and Mean-Shift, are used to prove the effectiveness of extracting spatio-temporal features for discriminating. (3) aiming at the asynchronous performance of different patient's facial expressions, this paper synchronizes and normalizes the start and end points of facial expressions. The experiments of multi-class data show that compared with content-based methods, the accuracy of temporal and spatial feature extraction, synchronization and normalization is higher than that of content-based method, and it is more practical for doctors. At the same time, the proposed method extends the application field of target tracking method and provides a new idea for quantitative evaluation of facial paralysis degree. The conclusion of the experiment is helpful for doctors to make reasonable judgment and treatment of the patient's condition.
【學位授予單位】：中南林業(yè)科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R745.12;TN713
本文編號：2148529
[Abstract]:Facial paralysis (facial paralysis) is a common clinical disease, according to statistics every 100000 people with 20 to 25 patients. The disease is usually caused by nerve damage, which can lead to severe sequelae if not properly treated at an early stage. If there is an effective method to evaluate the degree of facial paralysis objectively and quantitatively, it is very helpful for the treatment. In response to this demand, this paper uses facial expression data to extract more accurate feature information, and provides guidance for doctors to identify the degree of facial paralysis more objectively. The main research results are as follows: (1) aiming at the traditional research methods to locate the static image, Illumination, shadow and itself cause asymmetry. On the one hand, based on image content, a method based on LBP and Gabor filter is proposed to extract asymmetric features and motion features by (LBP Gabor). The advantages of LBP rotation invariance and gray invariance are used to remove the illumination effect, and the Gabor filter is used to reduce the noise and redundant information in the image processed by LBP. On the other hand, aiming at the nonuniformity of passband in Gabor filter, the feature extraction based on CMF (Concentric Modulation Filter) method is proposed, and LBP and CMF are combined with (LBP CMF). Because the passband of CMF is the same in all directions, it can extract more accurate features. The experimental results show that the LBP Gabor and LBP CMF methods are more effective and practical than the traditional methods. (2) the accuracy of feature extraction is affected by the uncorrelated regions in the still images (such as teeth in the mouth, shadows and nevus in the corners of the mouth). In order to solve these problems, based on the feature information of facial data, a target tracking method is proposed to extract temporal and spatial features for the first time. The method firstly selects Marker from the first frame of facial image, and then tracks the marker to extract temporal and spatial features in the image sequence. In this paper, two tracking methods, KLT and Mean-Shift, are used to prove the effectiveness of extracting spatio-temporal features for discriminating. (3) aiming at the asynchronous performance of different patient's facial expressions, this paper synchronizes and normalizes the start and end points of facial expressions. The experiments of multi-class data show that compared with content-based methods, the accuracy of temporal and spatial feature extraction, synchronization and normalization is higher than that of content-based method, and it is more practical for doctors. At the same time, the proposed method extends the application field of target tracking method and provides a new idea for quantitative evaluation of facial paralysis degree. The conclusion of the experiment is helpful for doctors to make reasonable judgment and treatment of the patient's condition.
【學位授予單位】：中南林業(yè)科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R745.12;TN713
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