本文關(guān)鍵詞：基于壓縮感知的醫(yī)學(xué)圖像測(cè)量矩陣及重構(gòu)算法研究　出處：《吉林大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 壓縮感知 測(cè)量矩陣 重構(gòu)算法 醫(yī)學(xué)圖像

【摘要】：現(xiàn)代信號(hào)處理應(yīng)用領(lǐng)域中，隨著信息技術(shù)的快速發(fā)展，使得數(shù)據(jù)量與日俱增，以奈奎斯特定理為指導(dǎo)的采樣方式導(dǎo)致采樣率過大，硬件系統(tǒng)難以實(shí)現(xiàn)，不能滿足實(shí)際的需要。醫(yī)學(xué)成像需要對(duì)病人的病變部位進(jìn)行反復(fù)掃描獲取采樣數(shù)據(jù)以重構(gòu)圖像，而長時(shí)間的掃描會(huì)給病人帶來較高的輻射甚至不適，因而尋求用較少的采樣數(shù)據(jù)精確重建出醫(yī)學(xué)圖像的方法，成為醫(yī)學(xué)圖像處理領(lǐng)域的研究熱點(diǎn)。 2006年，Candes等人提出壓縮感知理論(Compressed sensing, CS)，CS理論打破傳統(tǒng)奈奎斯特定理采樣率的束縛，通過設(shè)計(jì)一定的采樣矩陣，對(duì)信號(hào)進(jìn)行稀疏測(cè)量，再通過重構(gòu)算法對(duì)圖像進(jìn)行稀疏重建。CS理論在采樣同時(shí)對(duì)數(shù)據(jù)進(jìn)行壓縮，實(shí)現(xiàn)了低采樣率數(shù)據(jù)重構(gòu)。本文的主要工作和創(chuàng)新在于： 1、本文深入分析CS理論在醫(yī)學(xué)圖像重構(gòu)中應(yīng)用的可行性和合理性，分析了CS理論成功應(yīng)用于醫(yī)學(xué)圖像的本質(zhì)條件——醫(yī)學(xué)圖像良好的稀疏性，在此基礎(chǔ)上，本文重點(diǎn)研究了壓縮感知理論的兩個(gè)關(guān)鍵技術(shù)——測(cè)量矩陣和重構(gòu)算法，通過對(duì)常用測(cè)量矩陣和基本重構(gòu)算法的深入研究和分析，分別對(duì)測(cè)量矩陣和重構(gòu)算法提出了有效的改進(jìn)，使醫(yī)學(xué)成像在重構(gòu)質(zhì)量和重構(gòu)時(shí)間兩方面均有所提高。 2、本文介紹了目前常用的測(cè)量矩陣的構(gòu)造方式和基本結(jié)構(gòu)，并在采用相同的重構(gòu)算法條件下，對(duì)多幅醫(yī)學(xué)圖像進(jìn)行了壓縮感知重構(gòu)實(shí)驗(yàn)，對(duì)重構(gòu)效果進(jìn)行了評(píng)價(jià)分析；針對(duì)測(cè)量矩陣的特性，提出了基于奇異值分解的測(cè)量矩陣優(yōu)化方法，然后用優(yōu)化后的矩陣作為壓縮感知測(cè)量矩陣對(duì)醫(yī)學(xué)圖像進(jìn)行重構(gòu)，實(shí)驗(yàn)結(jié)果表明，經(jīng)過奇異值修正的矩陣與原矩陣相比較，重構(gòu)時(shí)間相當(dāng)?shù)臈l件下，重構(gòu)效果明顯增強(qiáng)，，峰值信噪比提高1~2dB。 3、本文對(duì)目前主要的兩大類壓縮感知重構(gòu)算法——貪婪算法和凸優(yōu)化算法進(jìn)行了梳理和介紹，深入研究了重構(gòu)算法基本思想和實(shí)現(xiàn)步驟，并對(duì)每種算法的優(yōu)缺點(diǎn)進(jìn)行了驗(yàn)證、評(píng)價(jià)和對(duì)比分析。提出一種新的基于最小全變分的醫(yī)學(xué)圖像重構(gòu)算法——SP_TV算法，該算法結(jié)合子空間匹配追蹤(SP)算法重構(gòu)速度快和最小全變分(TV)算法重構(gòu)效果好的優(yōu)點(diǎn)，實(shí)驗(yàn)結(jié)果表明，改進(jìn)后的重構(gòu)算法在重構(gòu)效果和重構(gòu)時(shí)間上均有明顯提高。
[Abstract]:In the field of modern signal processing application, with the rapid development of information technology, the amount of data is increasing day by day. The sampling method guided by Nyquist theorem leads to too large sampling rate, and the hardware system is difficult to realize. Medical imaging can not meet the actual needs. Medical imaging needs to repeatedly scan the lesions of patients to obtain sampling data in order to reconstruct the image, and long time scanning will bring high radiation and even discomfort to the patients. Therefore, it has become a research hotspot in the field of medical image processing to seek a method to reconstruct medical image accurately with less sampling data. In 2006, Candes et al put forward the compressed sensing theory, CS theory breaks the shackles of the traditional Nyquist theorem sampling rate. By designing a certain sampling matrix, the signal is measured sparsely, and then the sparse reconstruction. CS theory is used to compress the data at the same time. The main work and innovation of this paper are as follows: 1. This paper deeply analyzes the feasibility and rationality of CS theory applied in medical image reconstruction, and analyzes the essential condition of successful application of CS theory in medical image-the good sparsity of medical image. On this basis, this paper focuses on the two key technologies of compressed sensing theory, measurement matrix and reconstruction algorithm, through the in-depth study and analysis of common measurement matrix and basic reconstruction algorithm. Both the measurement matrix and the reconstruction algorithm are improved effectively, so that the reconstruction quality and the reconstruction time of the medical imaging are improved. 2. This paper introduces the construction method and basic structure of the measurement matrix which is commonly used at present, and carries on the compression perception reconstruction experiment to many medical images under the condition of the same reconstruction algorithm. The effect of reconstruction is evaluated and analyzed. According to the characteristics of the measurement matrix, an optimization method of the measurement matrix based on singular value decomposition is proposed, and then the optimized matrix is used as the compressed sensing measurement matrix to reconstruct the medical image. Compared with the original matrix modified by singular value, the reconstruction effect is obviously enhanced and the peak signal-to-noise ratio (PSNR) is increased by 1 ~ 2 dB under the condition that the reconstruction time is equal to that of the original matrix. 3. In this paper, two main kinds of compressed perceptual reconstruction algorithms, greedy algorithm and convex optimization algorithm, are reviewed and introduced, and the basic ideas and implementation steps of the reconstruction algorithm are deeply studied. The merits and demerits of each algorithm are verified, evaluated and compared. A new medical image reconstruction algorithm based on minimum total variation, SPSP _ TV algorithm, is proposed. The algorithm combines the advantages of fast reconstruction speed and good reconstruction effect of the subspace matching tracking (SPV) algorithm. The experimental results show that the proposed algorithm is effective. The reconstruction effect and time of the improved reconstruction algorithm are improved obviously.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.73

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 焦鵬飛;李亮;趙驥;;壓縮感知在醫(yī)學(xué)圖像重建中的最新進(jìn)展[J];CT理論與應(yīng)用研究;2012年01期

2 芮國勝;王林;田文飚;;一種基于基追蹤壓縮感知信號(hào)重構(gòu)的改進(jìn)算法[J];電子測(cè)量技術(shù);2010年04期

3 石光明;劉丹華;高大化;劉哲;林杰;王良君;;壓縮感知理論及其研究進(jìn)展[J];電子學(xué)報(bào);2009年05期

4 楊揚(yáng);劉哲;張萌;;一種基于全變差模型的欠采樣圖像重構(gòu)方法[J];紅外與毫米波學(xué)報(bào);2012年02期

5 高仕龍;;矩陣奇異值分解的圖像性質(zhì)及其應(yīng)用[J];樂山師范學(xué)院學(xué)報(bào);2008年05期

6 梁瑞宇;鄒采榮;王青云;張學(xué)武;;基于自適應(yīng)次梯度投影算法的壓縮感知信號(hào)重構(gòu)[J];信號(hào)處理;2010年12期

相關(guān)博士學(xué)位論文 前1條

1 劉晨軼;基于壓縮感知的OCT圖像稀疏重建[D];北京郵電大學(xué);2013年

本文編號(hào)：1407505