本文關(guān)鍵詞： 多模態(tài)圖像融合 NSCT變換 PCNN 拉普拉斯能量和 稀疏表示　出處：《江西財(cái)經(jīng)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)代科學(xué)技術(shù)的發(fā)展帶動(dòng)了醫(yī)療器械的蓬勃發(fā)展,其中的醫(yī)療影像診斷設(shè)備也絡(luò)繹不絕的出現(xiàn),使得醫(yī)療影像越來越多。醫(yī)療診斷中,病人需要拍攝不同醫(yī)療影像,這需要熟練專業(yè)的醫(yī)生才能根據(jù)這些醫(yī)療影像診斷病人的情況,而且醫(yī)生在診斷時(shí)需要綜合這些醫(yī)療影像中的信息,這對(duì)醫(yī)生特別是實(shí)習(xí)醫(yī)生是一個(gè)很大的挑戰(zhàn)。由此出現(xiàn)了醫(yī)學(xué)圖像融合技術(shù),其原理是將不同的醫(yī)療影像綜合在一起,把拍攝病人的醫(yī)療影像信息在一張醫(yī)療影像上顯示。本文主要對(duì)醫(yī)療影像融合算法中存在的問題進(jìn)行分析,通過對(duì)基于小波變換、超小波變換、鄰域熵脈沖耦合神經(jīng)網(wǎng)絡(luò)(PCNN)與稀疏表示等醫(yī)療影像融合算法進(jìn)行了解與思考,提出了兩種新的醫(yī)療影像融合方案。工作主要?jiǎng)?chuàng)新性工作如下:1.為了提高醫(yī)學(xué)圖像融合的質(zhì)量,提出一種基于非下采樣Contourlet變換(NSCT)與鄰域熵脈沖耦合神經(jīng)網(wǎng)絡(luò)(PCNN)的融合方法。在充分考慮圖像NSCT域系數(shù)特征和PCNN符合人眼的視覺特性基礎(chǔ)上,針對(duì)低、高頻分別提出一種基于改進(jìn)拉普拉斯能量和(Sum-Modified-Laplacian,SML)的低頻系數(shù)融合方法和一種基于鄰域熵的PCNN高頻系數(shù)融合方法,構(gòu)建一種全新的融合規(guī)則運(yùn)用于醫(yī)學(xué)圖像融合模型中。新的融合算法能取得更好的視覺效果以及較好的客觀指標(biāo)值,驗(yàn)證了算法的優(yōu)越性。2.提出了基于YUV變換和稀疏表示的結(jié)構(gòu)與功能醫(yī)學(xué)圖像融合算法。利用YUV變換保留功能圖像的顏色信息以及Y分量能夠代表源圖像的灰度信息的特性,結(jié)合NSCT變換實(shí)現(xiàn)Y分量與結(jié)構(gòu)圖像的融合,同時(shí)低頻采用稀疏表示的方法進(jìn)行低頻子帶融合,高頻采用局部能量的方法進(jìn)行融合。算法同時(shí)考慮到了色彩特征的保留以及空間分辨率的提升兩個(gè)方面,取得了較好的融合結(jié)果。
[Abstract]:The development of modern science and technology has led to the vigorous development of medical devices, in which medical imaging diagnostic equipment is also in constant stream, making more and more medical images. Patients need to take different medical images, which require a skilled professional doctor to diagnose the patient according to these medical images, and the doctor needs to synthesize the information in these medical images in the diagnosis. This is a great challenge for doctors, especially interns. From this, medical image fusion technology emerged, the principle of which is to integrate different medical images together. The medical image information of the patients is displayed on a medical image. This paper mainly analyzes the problems in the medical image fusion algorithm, through the wavelet transform, super-wavelet transform. Neighborhood entropy pulse coupled neural network (PCNN) and sparse representation of medical image fusion algorithm for understanding and thinking. Two new medical image fusion schemes are proposed. The main innovative work is as follows: 1. In order to improve the quality of medical image fusion. A neural network based on non-downsampling Contourlet transform and neighborhood entropy pulse coupling neural network (PCNN) is proposed. Based on the full consideration of the image NSCT domain coefficients and the visual characteristics of the PCNN in accordance with the human eye. For low and high frequency, a new method based on improved Laplace energy and Sum-Modified-Laplacian is proposed. SML-based low frequency coefficient fusion method and a neighborhood entropy based PCNN high frequency coefficient fusion method. A new fusion rule is constructed and applied to the medical image fusion model. The new fusion algorithm can achieve better visual effect and better objective index value. The superiority of the algorithm is verified. 2. A structural and functional medical image fusion algorithm based on YUV transform and sparse representation is proposed. The color information of the functional image and the Y component can be represented by the YUV transform. The characteristics of grayscale information of an image. Combining with NSCT transform, the fusion of Y component and structure image is realized. At the same time, sparse representation is used in low frequency subband fusion. The algorithm takes into account the preservation of color features and the enhancement of spatial resolution, and achieves good fusion results.
【學(xué)位授予單位】：江西財(cái)經(jīng)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：R445;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 魏交統(tǒng);陳平;潘晉孝;;基于主成分分析的遞變能量X射線圖像融合[J];中國體視學(xué)與圖像分析;2013年02期

2 李燕;陳瑩;董秀蘭;閆琰;;基于神經(jīng)網(wǎng)絡(luò)的遙感圖像識(shí)別算法[J];測(cè)繪與空間地理信息;2012年02期

3 李財(cái)蓮;孫即祥;康耀紅;;圖像多尺度幾何分析綜述[J];海南大學(xué)學(xué)報(bào)(自然科學(xué)版);2011年03期

4 胡朝芬;黃之杰;羅來華;;醫(yī)學(xué)圖像融合技術(shù)的臨床應(yīng)用[J];西南國防醫(yī)藥;2010年02期

5 屈小波;閆敬文;肖弘智;朱自謙;;非降采樣Contourlet域內(nèi)空間頻率激勵(lì)的PCNN圖像融合算法(英文)[J];自動(dòng)化學(xué)報(bào);2008年12期

6 高大偉;宋玉莉;;多傳感器圖像融合[J];科技信息;2008年31期

7 胡鋼;劉哲;徐小平;高瑞;;像素級(jí)圖像融合技術(shù)的研究與進(jìn)展[J];計(jì)算機(jī)應(yīng)用研究;2008年03期

8 朱瑞輝;萬敏;范國濱;;基于金字塔變換的圖像融合方法[J];計(jì)算機(jī)仿真;2007年12期

9 李萬社;王鳳蘭;;超小波發(fā)展綜述[J];陜西師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2007年03期

10 郭志強(qiáng);基于區(qū)域特征的小波變換圖像融合方法[J];武漢理工大學(xué)學(xué)報(bào);2005年02期

相關(guān)博士學(xué)位論文 前2條

1 王雷;多模態(tài)醫(yī)學(xué)圖像配準(zhǔn)與融合關(guān)鍵算法研究[D];華南理工大學(xué);2013年

2 韓亮;光學(xué)小波變換及其濾波器研究[D];重慶大學(xué);2008年

相關(guān)碩士學(xué)位論文 前1條

1 曹錚;模糊聚類及其在圖像分割中的應(yīng)用[D];河北大學(xué);2010年

，

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