基于詞典優(yōu)化算法對舌象特征提取的研究
發(fā)布時間：2019-02-22 09:16
【摘要】：隨著現(xiàn)代生活水平不斷提高,醫(yī)療作為一項(xiàng)基本民生,需求越來越大。而中醫(yī)是中華民族傳統(tǒng)文化的精髓之一,在某些方面具有西醫(yī)學(xué)不可取代的優(yōu)勢。因此,如何聯(lián)系當(dāng)代科學(xué)技術(shù)使中醫(yī)診斷現(xiàn)代化具有重要研究意義。舌診作為中醫(yī)學(xué)望診中的重要組成部分,舌診的客觀化不僅成為了國內(nèi)學(xué)者的研究熱點(diǎn),并引起了國外數(shù)理醫(yī)學(xué)界的關(guān)注。望舌診斷最為主要的判斷依據(jù)就是舌色,不同顏色的舌象可以表現(xiàn)出人的體質(zhì)差異與病癥程度。現(xiàn)在已有一些學(xué)者用HSI色彩空間來對舌色做分量劃分,但對于極相近的顏色特征邊界劃分不太明確。針對這個問題,文章主要做了以下工作:首先,為了更加準(zhǔn)確地對舌色進(jìn)行定量分析,本文基于稀疏表示和粒子濾波的思想提出了一種詞典優(yōu)化算法。該算法通過稀疏的線性表示建立了一個基礎(chǔ)理論模型。分割圖片時用取重疊塊的方法建立詞典集合,使選取的詞典塊能更大程度地包含特征信息。其次,論文處理特征信息時并未像現(xiàn)有的方法一樣直接對顏色進(jìn)行定性分析,而是基于蒙特卡洛粒子濾波的思想,以粒子作為特征信息,建立了一個KD樹作為粒子的集合,通過粒子的相似度來確定舌象的顏色分類。在完善了程序設(shè)計(jì)的條件下,本課題選取了三種常見舌象進(jìn)行了實(shí)驗(yàn),驗(yàn)證了本算法的可行性和有效性。最后,由于妊娠期糖尿病的特殊性,本課題選定它作為案例進(jìn)行分析。妊娠期糖尿病是一種初期病癥,它的舌象以紅舌和絳舌為主,在臨床上并不容易辨別。依據(jù)論文已建立的詞典優(yōu)化模型對妊娠期糖尿病舌圖像進(jìn)行實(shí)驗(yàn)的過程中,因其舌色特征的難辨性,在KD樹對匹配粒子查找時出現(xiàn)了工作量大、耗時長的問題。因此,本文在模型的求解上做了改進(jìn),引入加速近端梯度法,在模型的求解上做出了改進(jìn)。該方法屬于邊界化重采樣,可以在重要粒子區(qū)域進(jìn)行二次收斂,淘汰權(quán)重度偏低的粒子,留下權(quán)重度較高的粒子,降低了計(jì)算的復(fù)雜度,同時也提高了特征提取的準(zhǔn)確度。本課題所研究的舌色僅僅是中醫(yī)四診中望診的一個小部分,隨著計(jì)算機(jī)模式識別和圖像處理技術(shù)的不斷成熟,本課題采用的詞典算法也需要不斷的進(jìn)行優(yōu)化,從而進(jìn)一步提高特征提取的精度和效率,這也將是我們以后研究學(xué)習(xí)的一個重要的目標(biāo)。
[Abstract]:With the continuous improvement of modern living standards, medical care as a basic livelihood, the demand is growing. Traditional Chinese medicine is one of the quintessence of Chinese traditional culture and has irreplaceable advantages in some respects. Therefore, it is of great significance to study how to combine modern science and technology to modernize the diagnosis of traditional Chinese medicine. As an important part of traditional Chinese medicine (TCM) diagnosis, the objectification of tongue diagnosis has not only become the research hotspot of domestic scholars, but also attracted the attention of foreign medical circle. Tongue color is the most important basis of tongue diagnosis. Different tongue color can show the difference of physique and degree of disease. At present, some scholars have used HSI color space to divide tongue color components, but they are not clear about the very similar color feature boundaries. To solve this problem, the main work of this paper is as follows: firstly, in order to make quantitative analysis of tongue color more accurately, a dictionary optimization algorithm based on sparse representation and particle filter is proposed in this paper. The algorithm establishes a basic theoretical model by sparse linear representation. In the process of image segmentation, overlapping blocks are used to set up dictionary sets, so that the selected lexicon blocks can contain feature information to a greater extent. Secondly, when dealing with the feature information, the paper does not directly analyze the color qualitatively as the existing methods, but based on the Monte Carlo particle filter idea, taking the particle as the feature information, a KD tree is established as the set of particles. The color classification of tongue images is determined by the similarity of particles. Under the condition of perfect program design, three kinds of common tongue images are selected for experiments, and the feasibility and effectiveness of this algorithm are verified. Finally, due to the particularity of gestational diabetes mellitus, this topic selected it as a case study. Gestational diabetes mellitus (GDM) is an initial disease. Its tongue image is mainly red and crimson, which is not easy to distinguish clinically. During the experiment of tongue images of gestational diabetes mellitus (GDM) based on the dictionary optimization model established in this paper, due to the difficulty of distinguishing the tongue color features, it is difficult to find matching particles in the KD tree. Therefore, this paper improves the solution of the model, introduces the accelerated near-end gradient method, and improves the solution of the model. This method belongs to the boundary resampling and can converge twice in the important particle region, eliminate the particles with low weight degree, leave the particles with high weight degree, reduce the computational complexity and improve the accuracy of feature extraction. The tongue color studied in this paper is only a small part of the four diagnoses of TCM. With the development of computer pattern recognition and image processing technology, the dictionary algorithm used in this subject also needs to be optimized continuously. Thus, the accuracy and efficiency of feature extraction can be further improved, which will be an important goal for us to study in the future.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
[Abstract]:With the continuous improvement of modern living standards, medical care as a basic livelihood, the demand is growing. Traditional Chinese medicine is one of the quintessence of Chinese traditional culture and has irreplaceable advantages in some respects. Therefore, it is of great significance to study how to combine modern science and technology to modernize the diagnosis of traditional Chinese medicine. As an important part of traditional Chinese medicine (TCM) diagnosis, the objectification of tongue diagnosis has not only become the research hotspot of domestic scholars, but also attracted the attention of foreign medical circle. Tongue color is the most important basis of tongue diagnosis. Different tongue color can show the difference of physique and degree of disease. At present, some scholars have used HSI color space to divide tongue color components, but they are not clear about the very similar color feature boundaries. To solve this problem, the main work of this paper is as follows: firstly, in order to make quantitative analysis of tongue color more accurately, a dictionary optimization algorithm based on sparse representation and particle filter is proposed in this paper. The algorithm establishes a basic theoretical model by sparse linear representation. In the process of image segmentation, overlapping blocks are used to set up dictionary sets, so that the selected lexicon blocks can contain feature information to a greater extent. Secondly, when dealing with the feature information, the paper does not directly analyze the color qualitatively as the existing methods, but based on the Monte Carlo particle filter idea, taking the particle as the feature information, a KD tree is established as the set of particles. The color classification of tongue images is determined by the similarity of particles. Under the condition of perfect program design, three kinds of common tongue images are selected for experiments, and the feasibility and effectiveness of this algorithm are verified. Finally, due to the particularity of gestational diabetes mellitus, this topic selected it as a case study. Gestational diabetes mellitus (GDM) is an initial disease. Its tongue image is mainly red and crimson, which is not easy to distinguish clinically. During the experiment of tongue images of gestational diabetes mellitus (GDM) based on the dictionary optimization model established in this paper, due to the difficulty of distinguishing the tongue color features, it is difficult to find matching particles in the KD tree. Therefore, this paper improves the solution of the model, introduces the accelerated near-end gradient method, and improves the solution of the model. This method belongs to the boundary resampling and can converge twice in the important particle region, eliminate the particles with low weight degree, leave the particles with high weight degree, reduce the computational complexity and improve the accuracy of feature extraction. The tongue color studied in this paper is only a small part of the four diagnoses of TCM. With the development of computer pattern recognition and image processing technology, the dictionary algorithm used in this subject also needs to be optimized continuously. Thus, the accuracy and efficiency of feature extraction can be further improved, which will be an important goal for us to study in the future.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TP391.41
【參考文獻(xiàn)】
相關(guān)期刊論文 前10條
1 張國軍;鄭麗華;孫錫紅;;妊娠期糖尿病研究進(jìn)展[J];河北醫(yī)科大學(xué)學(xué)報(bào);2015年07期
2 徐杰;許家佗;朱蘊(yùn)華;陶楓;林sダ，
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