【摘要】：隨著計算機技術(shù)的不斷發(fā)展,CT成像技術(shù)已經(jīng)廣泛運用到臨床疾病診療中,成為了檢查腦疾病診斷的首選。但是CT成像掃描時X射線劑量的使用量也隨之增加,使得受檢人員誘發(fā)疾病幾率增大,而低劑量CT可降低它誘發(fā)疾病的幾率。但是低劑量的同時引起了圖像質(zhì)量的下降,因此對低劑量CT圖像進行恢復(fù)研究具有重大意義。由于稀疏表示和字典學(xué)習(xí)因其優(yōu)良特性而被應(yīng)用于圖像去噪、恢復(fù)等信號問題的求解,因此將該方法應(yīng)用于低劑量CT圖像問題具有重要意義和研究價值。本文為解決腦低劑量CT圖像降質(zhì)問題,對其恢復(fù)研究做了如下工作:首先,從基于字典學(xué)習(xí)(MOD、K-SVD、OLM、FDL-PG)的稀疏表示進行低劑量CT圖像恢復(fù)研究,結(jié)果表明FDL-PG算法相對其它算法在視覺感受和客觀上都更優(yōu),且適應(yīng)性好、收斂速度快,但仍存有一些噪聲和部分信息缺失等問題。然后,針對上述問題,本文提出了兩種改進的基于稀疏表示和字典學(xué)習(xí)的低劑量CT圖像恢復(fù)方法。一種方法是先對低劑量體模和臨床腦CT圖像進行主成分分析(PCA),再用降維后的數(shù)據(jù)進行字典訓(xùn)練(FDL-PG)并去噪。該方法(FDL-PG-PCA)增強了去噪性能,但仍有少量細節(jié)問題丟失。另一種方法是先對低劑量體模和臨床腦CT圖像進行BM3D濾波處理,再用濾波后的數(shù)據(jù)進行字典訓(xùn)練(FDL-PG)并去噪,該方法(FDL-PG-BM3D)對細節(jié)信息保持良好。通過對體模和臨床CT圖像實驗結(jié)果表明這兩種方法去噪性能較高,進一步抑制了噪聲。這兩種方法在臨床運用中,有望在保證醫(yī)生診斷準(zhǔn)確的同時減少受檢患者接收X射線輻射劑量。
[Abstract]:With the development of computer technology, CT imaging technology has been widely used in the diagnosis and treatment of clinical diseases, and has become the first choice for the diagnosis of brain diseases. However, the amount of X-ray dose used in CT scan also increases, which increases the probability of inducing disease, while low dose CT can reduce the probability of inducing disease. However, low dose leads to the deterioration of image quality, so it is of great significance to study the restoration of low dose CT images. Since sparse representation and dictionary learning are used to solve signal problems such as image denoising and restoring due to their excellent characteristics, it is of great significance and research value to apply this method to low-dose CT image problems. In order to solve the problem of brain low-dose CT image degradation, the following work has been done in this paper: firstly, low-dose CT image restoration is studied from sparse representation based on dictionary learning (MODK-SVDU OLMM-FDL-PG). The results show that the FDL-PG algorithm is better than other algorithms in visual perception and objectivity, and has good adaptability and fast convergence speed, but there are still some problems such as noise and lack of some information. Then, two improved low dose CT image restoration methods based on sparse representation and dictionary learning are proposed. One method is to perform principal component analysis (PCA),) on low dose phantom and clinical brain CT images, then to do dictionary training (FDL-PG) and denoising with dimensionally reduced data. This method (FDL-PG-PCA) improves the denoising performance, but there are still a few details lost. Another method is to process low dose phantom and clinical CT images with BM3D filter, then use filtered data for dictionary training (FDL-PG) and denoising. This method (FDL-PG-BM3D) maintains good detail information. The experimental results of phantom and CT images show that these two methods have high denoising performance and further suppress the noise. These two methods are expected to ensure the accuracy of the diagnosis and reduce the dose of X-ray radiation to the patients.
【學(xué)位授予單位】：東華理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

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