本文選題：骨齡評(píng)估 + 中華05��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：骨齡是評(píng)估青少年兒童體格發(fā)育程度的重要指標(biāo),在體育科學(xué)、司法和臨床醫(yī)學(xué)等領(lǐng)域有著廣泛的應(yīng)用。由于手腕骨可以較為準(zhǔn)確的反應(yīng)整體骨骼生長(zhǎng)發(fā)育情況,并具有易于拍攝、輻射劑量小等特點(diǎn),因此國(guó)內(nèi)外普遍將手腕骨發(fā)育情況作為骨齡評(píng)價(jià)標(biāo)準(zhǔn)。中華05計(jì)分法是目前我國(guó)對(duì)手腕骨骨齡評(píng)估所使用的標(biāo)準(zhǔn)方法。但使用該標(biāo)準(zhǔn)進(jìn)行評(píng)估需要對(duì)人員進(jìn)行專業(yè)培訓(xùn),熟練掌握各個(gè)發(fā)育分期的特征,整個(gè)過(guò)程十分繁瑣,而且存在主觀性較強(qiáng)、精確度低的缺點(diǎn),因此實(shí)現(xiàn)計(jì)算機(jī)自動(dòng)評(píng)估骨齡的需求不斷增長(zhǎng)。目前計(jì)算機(jī)自動(dòng)骨齡評(píng)估主要存在兩個(gè)困難。首先,CT圖像中手腕骨出現(xiàn)的位置、方向和大小不確定,并且在骨齡后期骨塊之間會(huì)出現(xiàn)重合的現(xiàn)象,對(duì)提取骨塊的分割與提取造成干擾。其次,骨齡標(biāo)準(zhǔn)中由人類語(yǔ)言所描述的不同等級(jí)生長(zhǎng)發(fā)育特征難以轉(zhuǎn)換成被計(jì)算機(jī)處理的圖像特征。針對(duì)以上問(wèn)題,本文提出了一種基于中華05計(jì)分法的計(jì)算機(jī)輔助橈骨等級(jí)評(píng)定方法,針對(duì)手腕骨CT圖像的橈骨特點(diǎn),對(duì)橈骨等級(jí)2至7級(jí)的自動(dòng)評(píng)估過(guò)程進(jìn)行了研究。本文的主要內(nèi)容如下:(1)提出了一種基于多模板約束局部模型的橈骨分割方法。針對(duì)不同骨齡階段橈骨形狀變化比較劇烈,約束局部模型不易收斂的問(wèn)題,提出一種多模板的約束局部模型算法,通過(guò)對(duì)橈骨圖像進(jìn)行預(yù)分類并與真實(shí)橈骨形狀接近的橈骨模型進(jìn)行分割。通過(guò)實(shí)驗(yàn)表明,該算法具有更快的收斂速度和更高的分割準(zhǔn)確度。(2)針對(duì)CT圖像背景單一的特點(diǎn),本文提出一種Haar隨機(jī)森林算法并將其作為約束局部模型的局部檢測(cè)器。通過(guò)實(shí)驗(yàn)表明,相比使用PCA或SVM作為局部檢測(cè)器的約束局部模型,該算法在分割CT圖像中的橈骨區(qū)域時(shí)具有更高的魯棒性和準(zhǔn)確度。(3)根據(jù)中華05計(jì)分法的橈骨等級(jí)評(píng)定標(biāo)準(zhǔn)選取了部分具有代表性的灰度特征和局部形狀特征,同時(shí)根據(jù)手腕骨CT圖像中橈骨特點(diǎn),提出了基于PCA的全局形狀特征和基于尺度變換不變紋理特征。然后設(shè)計(jì)了基于隨機(jī)森林的橈骨等級(jí)分類器,并通過(guò)實(shí)驗(yàn)對(duì)優(yōu)化模型參數(shù),驗(yàn)證了本文所提取的骨齡特征的分類性能。
[Abstract]:Bone age is an important index to evaluate the physical development of adolescents and children. It is widely used in sports science, justice and clinical medicine. Because the wrist bone can accurately reflect the whole bone growth and development, and has the characteristics of easy shooting and low radiation dose, the development of the hand wrist bone is generally regarded as the bone age evaluation standard at home and abroad. Zhonghua 05 scoring method is the standard method used to evaluate the bone age of the wrist bone in our country. However, the use of this standard for evaluation requires professional training of personnel, proficiency in the characteristics of various stages of development, and the fact that the whole process is cumbersome and has the disadvantages of being highly subjective and of low accuracy. Therefore, the demand for automatic evaluation of bone age by computer is increasing. At present, there are two main difficulties in automatic bone age assessment by computer. Firstly, the position, direction and size of the wrist bone in CT images are uncertain, and there will be overlap between the bone fragments in the later stage of bone age, which interferes with the segmentation and extraction of bone fragments. Secondly, the characteristics of growth and development of different grades described by human language in bone age standard are difficult to be converted into image features processed by computer. In order to solve the above problems, a computer-aided radial grading method based on Zhonghua 05 scoring method is proposed. According to the radial characteristics of CT images of wrist bone, the automatic evaluation process of radial grade 2 to 7 is studied. The main contents of this paper are as follows: (1) A method of radius segmentation based on multi-template constraint local model is proposed. A multi-template constrained local model algorithm is proposed to solve the problem that the radial shape changes dramatically at different bone ages and the constrained local model is not easy to converge. The radial image was preclassified and segmented from the real radial model. Experiments show that the algorithm has faster convergence speed and higher segmentation accuracy. Aiming at the single background of CT images, this paper presents a Haar stochastic forest algorithm and uses it as a local detector for constrained local models. The experimental results show that compared with using PCA or SVM as the constrained local model of local detector, The algorithm has higher robustness and accuracy in segmenting radius area in CT image. According to the radial grade evaluation standard of Zhonghua 05 score method, some representative gray scale features and local shape features are selected. According to the characteristics of radius in wrist CT images, the global shape feature based on PCA and the invariant texture feature based on scale transformation are proposed. Then, a radial classifier based on random forest is designed, and the model parameters are optimized by experiments to verify the classification performance of the bone age features extracted in this paper.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R68;TP391.7

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