本文選題：多層次自動(dòng)分割 + 法線方向校正��； 參考：《哈爾濱工業(yè)大學(xué)》2016年博士論文

【摘要】：醫(yī)學(xué)成像技術(shù)和計(jì)算機(jī)技術(shù)緊密結(jié)合使計(jì)算機(jī)醫(yī)學(xué)影像輔助技術(shù)在骨關(guān)節(jié)炎等疾病的診斷和治療等方面發(fā)揮巨大作用。高分辨率和高信噪比的MR及CT骨關(guān)節(jié)醫(yī)學(xué)圖像中含有大量圖像的相關(guān)信息,包括復(fù)雜的骨結(jié)構(gòu)、多變的骨形態(tài)、病灶位置和厚度等,然而大量的骨關(guān)節(jié)圖像信息遠(yuǎn)遠(yuǎn)不是醫(yī)生可以通過(guò)人工手段來(lái)處理的,醫(yī)生很難從醫(yī)學(xué)圖像中構(gòu)想出骨關(guān)節(jié)病變位置、軟骨厚度變化和鄰近組織狀態(tài),因此現(xiàn)代臨床診斷對(duì)骨關(guān)節(jié)結(jié)構(gòu)的檢測(cè)提出了高速度和高精度的技術(shù)要求,然而現(xiàn)有的骨結(jié)構(gòu)的檢測(cè)算法還無(wú)法達(dá)到實(shí)際臨床應(yīng)用的程度,尤其在針對(duì)緊密相鄰或者患有嚴(yán)重關(guān)節(jié)炎的骨結(jié)構(gòu)進(jìn)行檢測(cè)時(shí)會(huì)更加困難。針對(duì)上述問(wèn)題,本文以膝關(guān)節(jié)、髖關(guān)節(jié)和腕骨為研究對(duì)象,針對(duì)MR圖像研究精確的關(guān)節(jié)軟骨的自動(dòng)分割和厚度測(cè)量方法;針對(duì)CT圖像研究精確的關(guān)節(jié)骨的自動(dòng)分割方法,以便提高醫(yī)生的診斷精度和治療效果。本文提出了基于B樣條DGVF(DGVF-directional gradient vector flow)蛇模型的多層次三維圖像自動(dòng)分割算法,解決了膝、髖關(guān)節(jié)軟骨的分割問(wèn)題;提出了軟骨模型理論模擬方法,驗(yàn)證了零交叉方法不適合測(cè)量間隙狹窄的軟骨結(jié)構(gòu),會(huì)產(chǎn)生相當(dāng)大的誤差問(wèn)題;提出了新的基于誤差模型的平面內(nèi)的髖關(guān)節(jié)軟骨邊界檢測(cè)和厚度測(cè)量的方法,解決了極其接近骨結(jié)構(gòu)(股骨軟骨和髖臼軟骨)厚度測(cè)量問(wèn)題;最后提出了基于表面跟蹤校正與高斯標(biāo)準(zhǔn)差(SD)σ相結(jié)合的多階段自動(dòng)分割方法,解決了極其接近的骨結(jié)構(gòu)(髖關(guān)節(jié)和腕骨)的分割問(wèn)題。本文主要研究工作和成果如下:針對(duì)髖關(guān)節(jié)軟骨(股骨軟骨和髖臼軟骨)的分割,即解決極其接近和邊緣模糊的軟骨分割問(wèn)題,本文提出了基于B樣條方向梯度矢量流蛇模型的多層次三維圖像自動(dòng)分割算法。該分割方法主要包括圖像預(yù)處理、關(guān)節(jié)軟骨的粗分割和精確分割三個(gè)階段:第一階段使用非線性濾波方法和正弦插值算法解決圖像噪聲和各向同性問(wèn)題;第二階段使用Hessian矩陣三個(gè)特征值和最優(yōu)閾值化方法解決強(qiáng)化關(guān)節(jié)軟骨、確定軟骨位置并獲得緊鄰關(guān)節(jié)軟骨邊緣的初始輪廓線問(wèn)題;第三階段使用基于B樣條DGVF蛇模型的三維圖像分割算法提取關(guān)節(jié)軟骨邊緣輪廓。該自動(dòng)分割算法計(jì)算量少,可以對(duì)關(guān)節(jié)軟骨進(jìn)行有效的分割。針對(duì)目前世界上最常用的零交叉方法對(duì)髖關(guān)節(jié)軟骨厚度測(cè)量出現(xiàn)的問(wèn)題,本文提出的軟骨模型理論模擬方法驗(yàn)證了零交叉方法不適合測(cè)量間隙狹窄的軟骨結(jié)構(gòu),指出了零交叉方法產(chǎn)生厚度測(cè)量誤差大的原因。針對(duì)當(dāng)前廣泛應(yīng)用的零交叉方法對(duì)髖關(guān)節(jié)股骨軟骨和髖臼軟骨厚度測(cè)量存在的誤差問(wèn)題,本文提出了基于模型的平面內(nèi)的髖關(guān)節(jié)軟骨邊界檢測(cè)和厚度測(cè)量的新方法,簡(jiǎn)稱為基于誤差模型方法。該模型方法把軟骨厚度測(cè)量問(wèn)題轉(zhuǎn)化為對(duì)核磁共振數(shù)據(jù)中所觀察到的預(yù)測(cè)曲線和實(shí)際曲線之間的誤差問(wèn)題。當(dāng)軟骨表面任意邊緣點(diǎn)法線方向的模擬信號(hào)與實(shí)際軟骨邊緣點(diǎn)的法線方向信號(hào)的誤差最小的時(shí)候,就可獲得精確的平面內(nèi)軟骨邊緣位置和厚度�；谡`差模型的厚度測(cè)量方法可以克服相鄰薄結(jié)構(gòu)間的距離過(guò)小和系統(tǒng)固有的空間分辨率對(duì)厚度測(cè)量的限制。同時(shí)本文提出了一個(gè)新的三維軟骨厚度校正方法,糾正由于傾斜切片引起的對(duì)圖像平面厚度的過(guò)大估計(jì)問(wèn)題。針對(duì)髖關(guān)節(jié)和腕骨的分割,由于髖關(guān)節(jié)和腕骨內(nèi)部的緊密骨結(jié)構(gòu)使分割變得十分困難,本文提出了基于表面跟蹤校正與高斯標(biāo)準(zhǔn)差相結(jié)合的多階段自動(dòng)分割方法,解決了這類緊密相連的骨結(jié)構(gòu)(髖關(guān)節(jié)和腕骨)的分割問(wèn)題,能夠?yàn)橥饪迫P(guān)節(jié)置換手術(shù)計(jì)劃制定、術(shù)中導(dǎo)航以及術(shù)后評(píng)估提供重要信息。在表面跟蹤校正過(guò)程中,本文使用當(dāng)前點(diǎn)的幾何信息改善后續(xù)點(diǎn)表面法線方向估計(jì)的方法,使三維表面跟蹤算法能夠持續(xù)獲得后續(xù)點(diǎn)的信息,直到先前發(fā)現(xiàn)的點(diǎn)被重新訪問(wèn)或者某些條件不再滿足為止。因?yàn)樾Ｕň�方向的同時(shí)優(yōu)化了高斯標(biāo)準(zhǔn)差的值,所以本文的方法對(duì)噪聲圖像和關(guān)節(jié)嚴(yán)重退化造成的關(guān)節(jié)間隙狹窄的分割具有魯棒性。通過(guò)實(shí)驗(yàn)與目前最先進(jìn)的方法比較,本文的方法獲得了更高的分割精度。本文中的表面跟蹤校正與高斯標(biāo)準(zhǔn)差相結(jié)合的方法,以及在法線方向校正過(guò)程中獲取表面點(diǎn)的最佳尺度方法大大的提高了骨分割的效果。
[Abstract]:The combination of medical imaging technology and computer technology makes computer medical imaging aided technology play a great role in the diagnosis and treatment of diseases such as osteoarthritis. The MR and CT bone joint medical images with high resolution and high signal-to-noise ratio contain a large number of related information, including complex bone structure, variable bone morphology, and focus However, a large number of bone and joint image information is far from the doctor can handle by artificial means. It is difficult for doctors to conceive of the position of bone and joint lesions, the change of cartilage thickness and the state of adjacent tissues from medical images. Therefore, the modern clinical diagnosis of bone and joint structure detection puts forward high speed and high precision technique. However, the existing bone structure detection algorithms are still unable to achieve the actual clinical application, especially in the detection of bone structures which are closely adjacent to or suffering from severe arthritis. In this paper, the knee joint, hip joint and carpal bone are used as the research object to study the precise joint soft of the MR image. Automatic segmentation and thickness measurement of bone; an automatic segmentation method of joint bone for CT images is studied in order to improve the diagnostic accuracy and therapeutic effect of doctors. This paper proposes a multi-layer three-dimensional image segmentation algorithm based on the B spline DGVF (DGVF-directional gradient vector flow) snake model, which solves the knee and hip joint soft. It is proved that the zero crossing method is not suitable for measuring the narrow gap of the cartilage structure and produces considerable error problems. A new method for the detection of the cartilage boundary and the thickness measurement in the plane of the hip joint based on the error model is proposed, which solves the extremely close to the bone structure (femur). The thickness measurement problem of cartilage and acetabular cartilage); finally, a multi stage automatic segmentation method based on the combination of surface tracking correction and Gauss standard deviation (SD) Sigma was proposed to solve the extremely close bone structure (hip joint and carpal bone) segmentation problem. The main research work and results are as follows: the cartilage of the hip joint (femur cartilage and acetabulum soft) The segmentation of bone, that is, solves the problem of extremely close and blurred cartilage segmentation. In this paper, a multi-layer three-dimensional image segmentation algorithm based on the B spline direction gradient vector flow snake model is proposed. This segmentation method mainly includes three stages: image preprocessing, coarse segmentation and precise segmentation of articular cartilage: the first stage uses nonlinear filtering. Wave method and sinusoidal interpolation algorithm solve the problem of image noise and isotropy; the second stage uses three eigenvalues of Hessian matrix and optimal threshold method to solve the problem of strengthening the articular cartilage, determining the position of cartilage and obtaining the initial contour of the adjacent cartilage edge, and the third stage uses the 3D map of the B spline DGVF snake model. The segmentation algorithm is used to extract the contour of the articular cartilage edge. This automatic segmentation algorithm is less calculated and can effectively segment the articular cartilage. In view of the problem that the most commonly used zero crossing method in the world appears in the measurement of the thickness of the cartilage of the hip joint, the theory simulation method of cartilage model proposed in this paper proves that the zero crossing method is not suitable for measurement. The reason for the large error in measuring the thickness of the thickness of the zero crossing method is pointed out. In this paper, a new method for measuring the thickness of the cartilage and acetabular cartilage in the hip joint is proposed. A new method of measuring the cartilage boundary and measuring the thickness of the hip joint in the plane is proposed. The method, for short, is based on the error model method, which transforms the problem of the measurement of cartilage thickness to the error between the predicted curve and the actual curve observed in the magnetic resonance data. The error of the simulated signal in the normal direction of the arbitrary edge point of the cartilage surface and the normal direction signal of the actual cartilage edge point is minimal. At the same time, the precise edge position and thickness of the cartilage in the plane can be obtained. The thickness measurement method based on the error model can overcome the limitation of the distance between the adjacent thin structures and the inherent spatial resolution of the system to the thickness measurement. At the same time, a new method for correcting the thickness of the three-dimensional soft bone is proposed to correct the slant slice. Due to the large estimation of the thickness of the image plane, the segmentation of the hip and carpal bone is very difficult because of the tight bone structure in the hip and carpal bones. This paper proposes a multi stage automatic cutting method based on the combination of surface tracking correction and Gauss standard deviation, which solves the closely connected bone structure. The segmentation of hip and carpal bone can provide important information for the surgical planning of total joint replacement surgery, navigation and postoperative evaluation. In the process of surface tracking correction, this paper uses the geometric information of the current point to improve the square method of the direction estimation of the following point surface, so that the 3D surface tracking algorithm can be continuously obtained. The information of the continuation point, until the previously discovered point is revisited or some conditions are no longer satisfied. Since the normals are corrected while optimizing the values of the Gauss standard deviation, this method is robust to the segmentation of the joint gap narrowing caused by the noise image and the severe joint degradation. Compared with the method, the method obtained in this paper has higher segmentation accuracy. In this paper, the method of combining surface tracking correction with Gauss standard deviation and the optimal scaling method to obtain surface points in the normal direction correction process greatly improve the effect of bone segmentation.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R816.8;TP391.41

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