本文選題：甲狀腺結(jié)節(jié)　切入點(diǎn)：射頻信號(hào)　出處：《南京大學(xué)》2017年碩士論文

【摘要】：隨著超聲技術(shù)的不斷發(fā)展及人們健康意識(shí)的提高,甲狀腺結(jié)節(jié)在超聲檢查中的發(fā)現(xiàn)率已達(dá)70%以上,其中惡性約占5%。當(dāng)前,甲狀腺惡性結(jié)節(jié)(甲狀腺癌)對(duì)人類身心健康已經(jīng)構(gòu)成了重大威脅。醫(yī)學(xué)超聲成像能夠表現(xiàn)出下列優(yōu)勢(shì):無(wú)損性、可重復(fù)性好、實(shí)時(shí)性、廉價(jià)性、靈敏度高等,常常應(yīng)用于甲狀腺結(jié)節(jié)良惡性的鑒別中。目前,甲狀腺結(jié)節(jié)良惡性鑒別的金標(biāo)準(zhǔn)是細(xì)針穿刺抽吸活檢(FNAB),為減少患者的痛苦,縮短檢查周期,利用B超脈沖回波RF信號(hào)中的特征量以及神經(jīng)網(wǎng)絡(luò)等信號(hào)處理技術(shù)對(duì)甲狀腺結(jié)節(jié)良惡性進(jìn)行判斷,有助于為臨床醫(yī)生更準(zhǔn)確、更全面的做出診斷提供輔助信息。本研究利用甲狀腺B超脈沖回波RF信號(hào),采用時(shí)域、頻域及非線性分析方法和BP神經(jīng)網(wǎng)絡(luò)識(shí)別技術(shù),探討一種新的甲狀腺結(jié)節(jié)良惡性鑒別方法。選取甲狀腺結(jié)節(jié)感興趣區(qū)域內(nèi)的B超脈沖回波RF信號(hào),提取多個(gè)特征量,如期望值、低頻小波系數(shù)均值、小波模極大值均值和最大李雅普諾夫指數(shù),并利用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行甲狀腺結(jié)節(jié)識(shí)別量化。實(shí)驗(yàn)結(jié)果表明,以上特征量均能有效地對(duì)甲狀腺結(jié)節(jié)進(jìn)行良惡性鑒別,其識(shí)別率分別高達(dá)95.1%,92.7%,97.6%及82.9%。其中,期望值、低頻小波系數(shù)均值、小波模極大值均值對(duì)2級(jí)甲狀腺結(jié)節(jié)的識(shí)別率達(dá) 100%。本文工作為診斷甲狀腺結(jié)節(jié)良惡性提供了一個(gè)新的思路,具有重要的臨床應(yīng)用價(jià)值。
[Abstract]:With the development of ultrasound technology and the improvement of people's health awareness, the detection rate of thyroid nodules in ultrasound examination has reached more than 70%, of which malignancy accounts for about 5%.At present, malignant thyroid nodules (thyroid carcinoma) have posed a major threat to human physical and mental health.Medical ultrasound imaging can show the following advantages: nondestructive, reproducible, real-time, cheap, high sensitivity and so on. It is often used in the differential diagnosis of benign and malignant thyroid nodules.At present, the gold standard for differentiating benign and malignant thyroid nodules is fine needle aspiration biopsy (FNABN).The diagnosis of benign and malignant thyroid nodules by using the characteristic quantity of RF signal of B-mode ultrasonic echo and neural network is helpful to provide more accurate and comprehensive diagnosis information for clinicians.In this study, a new method for differentiating benign and malignant thyroid nodules was studied by using time-domain, frequency-domain and nonlinear analysis methods and BP neural network.The RF signal of B-mode ultrasonic echo in the region of interest of thyroid nodule is selected, and several characteristic quantities are extracted, such as expectation value, mean of low frequency wavelet coefficient, mean value of wavelet modulus maximum and maximum Lyapunov exponent.BP neural network was used to identify thyroid nodules.The experimental results show that the above characteristic quantities can effectively distinguish benign and malignant thyroid nodules, and the recognition rates are as high as 95.1% and 82.9%, respectively.Among them, the expected value, the mean of low frequency wavelet coefficient and the mean of wavelet modulus maximum value are 100% for the recognition of thyroid nodule of grade 2.This work provides a new idea for the diagnosis of benign and malignant thyroid nodules and has important clinical application value.
【學(xué)位授予單位】：南京大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R581;R445.1
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本文編號(hào)：1690113