【摘要】：海馬體病變與神經(jīng)疾病息息相關(guān),海馬體解剖結(jié)構(gòu)的不規(guī)則性以及與周圍組織結(jié)構(gòu)如杏仁體邊界模糊增加了分割海馬體的難度。目前較流行的圖像分割算法較適用于分割規(guī)則器官或大器官,而海馬體體積較小,形狀不規(guī)則,因此常用的圖像分割算法不能達到理想的分割精度。而在常用的基于圖譜的分割算法中多以基于灰度的描述子描述圖像特征,基于灰度的圖像特征在描述亮暗不均勻圖譜時辨識度差,本論文提出一種新的識別度較高的圖像特征描述子——方向-尺度描述子(orientation-scale descriptor OSD),然后結(jié)合稀疏編碼算法提出一種新的基于方向尺度描述子和稀疏編碼(orientation-scale descriptor and spare coding OSDSC)海馬體分割算法,提高海馬體分割精度。不同于主流的基于字典學習的方法,OSDSC算法用同時包含灰度紋理信息和空間結(jié)構(gòu)信息的方向-尺度描述子(orientation-scale descriptor OSD)代替低維特征來描述像素特征,OSD的優(yōu)點是它同時包含多種低維特征且能降低圖譜間灰度不均勻性的影響。OSDSC算法包括四個步驟:首先,圖像預處理。第二,特征提取:提取待分割圖像像素和圖譜圖像像素的方向-尺度描述子。第三,字典構(gòu)建及稀疏編碼:選取圖譜像素的方向-尺度描述子為目標像素構(gòu)建特有字典,用特有字典近似表達即重建目標像素并得到稀疏編碼系數(shù);第四,標號融合及閾值判定。融合圖譜像素的標號和編碼系數(shù)得到目標像素的標號估計值;閾值判定估計值完成分割。為了驗證OSDSC算法分割的準確性,分別用OSDSC算法,Simple,Major Voting,Staple,Collate算法分割MICCAI數(shù)據(jù)庫中海馬體,以Dice值作為分割評判標準,實驗結(jié)果表明OSDSC方向-尺度描述子的分割精度高于Simple,Major Voting,Staple,Collate 算法。
[Abstract]:Hippocampal lesions are closely related to neurological diseases. The irregularity of hippocampal anatomical structure and the blurring of the surrounding tissue such as amygdala increase the difficulty of hippocampal body segmentation. At present, the popular image segmentation algorithms are more suitable for regular organs or large organs, but the hippocampal body is small in volume and irregular in shape, so the commonly used image segmentation algorithms can not achieve the ideal segmentation accuracy. However, the image features are often described by grayscale based descriptors, and the recognition degree of grayscale based image features is poor when describing the non-uniform spectrum of light and dark. In this paper, a new image feature descriptor with high recognition, direction-scale descriptor (orientation-scale descriptor OSD), and a new directional scale descriptor and sparse coding (orientation-scale descriptor and spare coding) based on sparse coding algorithm are proposed. OSDSC) Hippocampal Segmentation algorithm, Improve the accuracy of hippocampal segmentation. Different from the mainstream Dictionary Learning-based approach, OSD algorithm uses direction-scale descriptors that contain both grayscale texture information and spatial structure information (orientation-scale descriptor OSD) instead of low-dimensional features to describe pixel features) the advantage of OSD is that it simultaneously packets. The OSDSC algorithm includes four steps: firstly, it can reduce the influence of grayscale heterogeneity between maps. Image preprocessing. Second, feature extraction: extracting the direction-scale descriptor of pixels of image to be segmented and pixels of atlas image. Thirdly, dictionary construction and sparse coding: selecting the direction-scale descriptor of map pixels as target pixels to construct special dictionaries, using the approximate representation of unique dictionaries to reconstruct target pixels and obtain sparse coding coefficients; fourth, Label fusion and threshold determination. The label estimation value of the target pixel is obtained by combining the labeling and coding coefficients of the map pixels, and the threshold decision estimation value is segmented. In order to verify the accuracy of OSDSC segmentation, the Dice algorithm is used to segment the hippocampus in MICCAI database using OSDSC algorithm. The experimental results show that the segmentation accuracy of OSDSC direction-scale descriptor is higher than that of simple Major Votingling StapleCollate algorithm.
【學位授予單位】：南方醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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本文編號：2177891