本文選題：稀疏表示 + 醫(yī)學(xué)圖像融合��； 參考：《浙江理工大學(xué)》2017年碩士論文

【摘要】：隨著醫(yī)學(xué)成像設(shè)備的快速發(fā)展和普及,醫(yī)學(xué)影像技術(shù)已成為臨床醫(yī)學(xué)中檢查和診斷疾病必不可少的手段,然而單一模態(tài)的醫(yī)學(xué)圖像所提供信息存在一定局限性,為此,學(xué)者們提出了醫(yī)學(xué)圖像融合。醫(yī)學(xué)圖像融合是將不同模態(tài)醫(yī)學(xué)圖像之間各自優(yōu)勢進行相互融合,彌補單一模態(tài)的醫(yī)學(xué)圖像的局限性,從而在單幅醫(yī)學(xué)圖像中更加直觀地提供人體解剖結(jié)構(gòu)、生理狀況及病理特性等信息。由于稀疏表示能夠提取少數(shù)特征用于表示圖像全部信息,因此本文將稀疏表示理論與圖像融合技術(shù)相結(jié)合,并進行深入研究,主要內(nèi)容如下:1)針對醫(yī)學(xué)圖像復(fù)雜多樣性特點,提出一種基于在線字典學(xué)習(xí)的自適應(yīng)醫(yī)學(xué)圖像融合算法。首先利用在線字典學(xué)習(xí)算法訓(xùn)練源圖像的過完備字典,提高圖像特征提取的自適應(yīng)能力;然后利用OMP算法對源圖像進行稀疏表示得到稀疏編碼,降低了融合數(shù)據(jù)維度;再根據(jù)源圖像之間稀疏編碼的能量差異程度和梯度差異程度自適應(yīng)調(diào)整融合規(guī)則,若能量差異程度大于梯度差異程度,則根據(jù)能量取大規(guī)則融合稀疏編碼,反之,根據(jù)梯度取大規(guī)則融合稀疏編碼;最后將融合后的稀疏編碼與過完備字典進行重構(gòu)得到融合圖像。實驗結(jié)果表明:與多尺度幾何分析、K奇異值分解等圖像融合算法比較,本文算法融合的圖像客觀評價指標信息熵、邊緣評價因子均有所提高,主觀上紋理清晰、對比度高,能夠較好的保留源圖像邊緣信息。2)針對ROMP算法在壓縮感知重構(gòu)中需預(yù)估稀疏度導(dǎo)致重構(gòu)精度不穩(wěn)定的問題,提出一種改進的ROMP算法。由于觀測信號能夠繼承原始信號特征,在選擇候選集原子過程中引入自適應(yīng)弱選擇標準,依據(jù)觀測信號的信息量設(shè)定弱選擇標準,實現(xiàn)稀疏度自適應(yīng)調(diào)整。將該算法應(yīng)用于壓縮感知框架下的醫(yī)學(xué)圖像融合,并提出一種結(jié)合觀測信號結(jié)構(gòu)相似度的融合規(guī)則,當待融合的觀測信號之間結(jié)構(gòu)相似度較高時,說明待融合的原始信號之間同樣具有相似性,以兩者信息量的加權(quán)作為融合規(guī)則。同理,當待融合的觀測信號結(jié)構(gòu)相似度較低時,選擇信息量較大的觀測信號作為融合后的觀測信號。實驗結(jié)果表明:改進ROMP算法的重構(gòu)圖像質(zhì)量優(yōu)于OMP、ROMP、SAMP等算法,其峰值信噪比提高了6%左右。應(yīng)用于醫(yī)學(xué)圖像融合時,得到融合圖像具有較好的人類視覺特性,輪廓清晰,保留了源圖像中大部分特征信息,可在較短時間內(nèi)得到優(yōu)質(zhì)的融合結(jié)果。
[Abstract]:With the rapid development and popularization of medical imaging equipment, medical imaging technology has become an indispensable means for the examination and diagnosis of diseases in clinical medicine. However, the information provided by single mode medical images has some limitations.Scholars have proposed medical image fusion.Medical image fusion is to fuse the advantages of different medical images to make up for the limitation of single mode medical image, so as to provide the anatomical structure of human body more intuitively in a single medical image.Physiological and pathological information.Because sparse representation can extract a few features to represent all the information of an image, this paper combines sparse representation theory with image fusion technology, and makes a thorough study. The main contents are as follows: 1) aiming at the complex diversity of medical image,An adaptive medical image fusion algorithm based on online dictionary learning is proposed.Firstly, the online dictionary learning algorithm is used to train the over-complete dictionary of the source image to improve the adaptive ability of image feature extraction, and then the sparse representation of the source image is obtained by using OMP algorithm, which reduces the dimension of fusion data.Then adaptively adjusts the fusion rules according to the degree of energy difference and gradient difference between source images. If the degree of energy difference is greater than the gradient difference degree, then according to the large rule of energy fusion sparse coding, conversely,Finally, the fused sparse coding is reconstructed from the over-complete dictionary to obtain the fused image.The experimental results show that compared with the image fusion algorithms such as multi-scale geometric analysis and singular value decomposition, the objective evaluation index information entropy and edge evaluation factors of this algorithm are improved, the subjective texture is clear, and the contrast is high.A modified ROMP algorithm is proposed to solve the problem that the ROMP algorithm needs to predict the sparse degree in the compression perception reconstruction, which leads to the instability of the reconstruction accuracy. 2) A modified ROMP algorithm is proposed to solve the problem that the reconstruction accuracy is unstable due to the need to predict the sparsity of the ROMP algorithm.Because the observed signal can inherit the characteristics of the original signal, adaptive weak selection criterion is introduced in the process of selecting candidate set atoms, and the weak selection criterion is set according to the amount of information of the observed signal to realize the adaptive adjustment of the sparsity.The algorithm is applied to the medical image fusion under the frame of compressed perception, and a fusion rule combining the structural similarity of the observed signals is proposed. When the structural similarity of the observed signals to be fused is high,It is shown that the original signals to be fused have the same similarity, and the weighted information between them is taken as the fusion rule.Similarly, when the structural similarity of the observed signals to be fused is low, the observation signals with large amount of information are selected as the observed signals after fusion.The experimental results show that the reconstructed image quality of the improved ROMP algorithm is better than that of the ROMP algorithm, and the peak signal-to-noise ratio (PSNR) is improved by about 6%.When applied to medical image fusion, the fused image has better human visual characteristics, clear contour, and retains most of the feature information in the source image, which can obtain high quality fusion results in a relatively short time.
【學(xué)位授予單位】：浙江理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻】

相關(guān)期刊論文 前10條

1 戴文戰(zhàn);姜曉麗;李俊峰;;基于人眼視覺特性的NSCT醫(yī)學(xué)圖像自適應(yīng)融合[J];電子學(xué)報;2016年08期

2 殷明;段普宏;褚標;梁翔宇;;結(jié)合SIST和壓縮感知的CT與MRI圖像融合[J];光電工程;2016年08期

3 趙春暉;郭蘊霆;;一種快速的基于稀疏表示和非下采樣輪廓波變換的圖像融合算法[J];電子與信息學(xué)報;2016年07期

4 宗靜靜;邱天爽;郭冬梅;;聯(lián)合稀疏表示的醫(yī)學(xué)圖像融合及同步去噪[J];中國生物醫(yī)學(xué)工程學(xué)報;2016年02期

5 趙學(xué)軍;李育珍;雷書_g;;基于遺傳算法優(yōu)化的稀疏表示圖像融合算法[J];北京郵電大學(xué)學(xué)報;2016年02期

6 陳木生;;結(jié)合NSCT和壓縮感知的紅外與可見光圖像融合[J];中國圖象圖形學(xué)報;2016年01期

7 楊勇;童松;黃淑英;;快速離散Curvelet變換域的圖像融合[J];中國圖象圖形學(xué)報;2015年02期

8 練秋生;石保順;陳書貞;;字典學(xué)習(xí)模型、算法及其應(yīng)用研究進展[J];自動化學(xué)報;2015年02期

9 李俊峰;姜曉麗;戴文戰(zhàn);;基于提升小波變換的醫(yī)學(xué)圖像融合[J];中國圖象圖形學(xué)報;2014年11期

10 李奕;吳小俊;;香農(nóng)熵加權(quán)稀疏表示圖像融合方法研究[J];自動化學(xué)報;2014年08期

，

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