本文選題：髕骨脫位及半脫位 + 髕骨CT圖像　； 參考：《華北水利水電大學(xué)》2017年碩士論文

【摘要】：髕骨損傷常見于運動較多、量較大的群體當中,尤其是年輕人,易引起膝關(guān)節(jié)功能性障礙,而髕骨損傷最常見的臨床表現(xiàn)則是髕骨脫位,若未得到及時正確的處理,則會帶來髕骨不穩(wěn)、復(fù)發(fā)性脫位等一系列后遺癥,復(fù)發(fā)率高達20%-40%,給病人生活帶來極大不便。計算機技術(shù)與醫(yī)療診斷相融合的CT(computerized tomography)、MRI(Magnetic resonance imaging)、PET(Positron emission tomography)及DSA(Digital subtraction angiography)等醫(yī)學(xué)成像技術(shù)的出現(xiàn)及應(yīng)用使得醫(yī)生能夠更加直觀、清晰地看到病變部位,可極大地提高臨床診斷正確率和病變治愈率,減少醫(yī)生工作量,提高醫(yī)生工作效率。本文設(shè)計了一個髕骨脫位輔助診斷系統(tǒng)。首先對比分析研究幾種經(jīng)典的圖像濾波方法、對比度增強算法及幾種常用圖像分割方法的優(yōu)缺點,選用高斯濾波、基于局部區(qū)域的對比度增強算法對髕骨CT圖像進行預(yù)處理,以降低圖像噪聲、增強圖像對比度;在此基礎(chǔ)上,依次利用基于邊緣檢測的圖像分割算法、包圍盒算法精確分割出髕骨CT圖像中目標區(qū)域的邊緣輪廓;最后通過具體實驗分析Harris角點檢測算法、梯度檢測方法選取邊緣輪廓關(guān)鍵點的缺陷,提出了基于不同區(qū)域的關(guān)鍵點提取方法。利用該方法對圖像分割階段得到的邊緣輪廓關(guān)鍵點進行提取,最終以關(guān)鍵點為基準采用平面擬合方法,通過數(shù)據(jù)量化及計算測量,得到髕骨股骨中軸面夾角、距離及髕骨傾斜角,為髕骨脫位診斷提供有效輔助。通過實驗得到的測量數(shù)據(jù)與醫(yī)學(xué)臨床判斷標準的對比。本文研究對于髕骨輪廓規(guī)則、特征點較為明顯的髕骨CT圖片,可獲得較為精確的測量數(shù)據(jù),能夠有效輔助髕骨脫位診斷。最后,通過測量數(shù)據(jù)與判斷標準的實驗對比總結(jié)可知:對于髕骨輪廓較正、特征點較為明顯的髕骨CT圖片,本文研究設(shè)計的髕骨脫位計算機輔助診斷系統(tǒng)具有較高的正確性、實踐意義及臨床使用價值。
[Abstract]:Patellar injuries, especially among young people, tend to cause functional disorders of the knee joint, and the most common clinical manifestation of patellar injuries is dislocation of patella, if not properly dealt with in time, the most common clinical manifestation of patellar injury is dislocation of patella, especially among young people, and the most common clinical manifestation of patellar injury is dislocation of patella.Will bring patella instability, recurrent dislocation and other sequelae, recurrence rate up to 20-40, bring great inconvenience to the patient's life.The appearance and application of medical imaging techniques, such as CT(computerized tomphography and DSA(Digital subtraction emission, which combine computer technology with medical diagnosis, enable doctors to see the location of lesions more intuitively and clearly.It can greatly improve the correct rate of clinical diagnosis and cure rate of diseases, reduce the workload of doctors and improve the efficiency of doctors.An auxiliary diagnosis system for patellar dislocation is designed in this paper.Firstly, the advantages and disadvantages of several classical image filtering methods, contrast enhancement algorithm and several common image segmentation methods are compared and studied. Gao Si filter and contrast enhancement algorithm based on local region are used to preprocess patellar CT image.In order to reduce image noise and enhance image contrast, image segmentation algorithm based on edge detection and bounding box algorithm are used to accurately segment the edge contour of patellar CT image.Finally, the Harris corner detection algorithm and the gradient detection method are analyzed in detail to select the defects of the key points of the edge contour, and a key point extraction method based on different regions is proposed.This method is used to extract the key points of the edge contour in the image segmentation stage. Finally, the angle of the central axial plane of the patella femur is obtained by using the plane fitting method, which is based on the key points, and through the quantization of the data and the calculation and measurement.Distance and patellar angle provide effective assistance for diagnosis of patellar dislocation.The comparison between the measured data obtained from experiments and the standard of medical clinical judgment.In this paper, the patellar CT images with regular patellar contours and obvious characteristic points can obtain more accurate measurement data and can effectively assist in the diagnosis of patellar dislocation.Finally, by comparing the measured data with the standard of judgment, we can conclude that the computer aided diagnosis system for patellar dislocation is correct for the CT images of patella whose profile is more accurate and characteristic points are more obvious.Practical significance and clinical use value.
【學(xué)位授予單位】：華北水利水電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R684;TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 韓劍輝;吳振;李蘭英;;雙目立體匹配算法的FPGA實現(xiàn)[J];哈爾濱理工大學(xué)學(xué)報;2016年04期

2 郭寬;;數(shù)字圖像處理技術(shù)在醫(yī)學(xué)領(lǐng)域的應(yīng)用[J];科技與創(chuàng)新;2016年13期

3 姚倩;侯筱婷;張娟;王麗;;基于增強補種的混合水平集CT圖像分割[J];計算技術(shù)與自動化;2016年02期

4 周慧文;包呼日查;王永祥;王偉;魏寶剛;馬秉賢;呂飛;胡永恒;徐永勝;;青少年急性髕骨脫位治療進展[J];內(nèi)蒙古醫(yī)學(xué)雜志;2016年03期

5 嚴廣斌;;髕骨的形態(tài)和厚度[J];中華關(guān)節(jié)外科雜志(電子版);2016年01期

6 王明;何輝;王林飛;劉前坤;;高斯—拉普拉斯算子場源邊界識別方法[J];物探與化探;2015年S1期

7 趙莉;何緒成;張彥;;改良型髕骨軸位投照法在診斷髕骨脫位半脫位中的應(yīng)用價值[J];海南醫(yī)學(xué);2015年23期

8 趙建寧;陳爍;;髕骨脫位的規(guī)范化治療方案[J];中國骨傷;2015年07期

9 秦樂;李梅;王慧;姚偉武;沈繼;;CT在髕骨脫位三聯(lián)術(shù)后評估中的價值[J];實用放射學(xué)雜志;2015年07期

10 呂飛;徐永勝;;髕股關(guān)節(jié)不穩(wěn)定的臨床治療研究進展[J];內(nèi)蒙古醫(yī)學(xué)雜志;2015年03期

相關(guān)碩士學(xué)位論文 前10條

1 劉龍;醫(yī)學(xué)斷層影像的自相似去噪方法研究[D];山東大學(xué);2016年

2 霍榮;基于改進非線性增益和雙邊濾波器的自適應(yīng)圖像廣義模糊增強[D];海南大學(xué);2016年

3 胡偉生;基于交叉視覺皮質(zhì)模型圖像融合算法研究[D];浙江工商大學(xué);2015年

4 孔琪;面向髕骨脫位診斷的骨科CT圖像自動測量[D];山東大學(xué);2015年

5 肖碩;基于數(shù)據(jù)流的圖像特征值提取與索引構(gòu)建方法研究[D];華中科技大學(xué);2015年

6 袁亞鋒;聯(lián)合手術(shù)治療復(fù)發(fā)性髕骨脫位[D];鄭州大學(xué);2015年

7 張斌;醫(yī)學(xué)圖像邊緣檢測算法的研究與實現(xiàn)[D];東北石油大學(xué);2012年

8 彭瑩瑩;醫(yī)學(xué)圖像部分容積效應(yīng)校正方法研究及其應(yīng)用[D];南方醫(yī)科大學(xué);2011年

9 孫永偉;基于最小包圍盒及自適應(yīng)聚類的三維R~*-樹索引結(jié)構(gòu)[D];山東理工大學(xué);2011年

10 王春燕;活動形狀模型算法在醫(yī)學(xué)圖像分割中的應(yīng)用研究[D];華南理工大學(xué);2010年

，

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