本文選題：融合醫(yī)學(xué)圖像特征 + 病灶區(qū)域特征��； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：目前在醫(yī)學(xué)領(lǐng)域,醫(yī)學(xué)成像技術(shù)已經(jīng)成為醫(yī)生進(jìn)行醫(yī)學(xué)診斷最為信賴的輔助技術(shù)之一。各大醫(yī)院每天都在借助如CT,核磁共振等各種成像技術(shù)來(lái)生成不同格式、大小的醫(yī)學(xué)圖像。這些海量的醫(yī)學(xué)圖像蘊(yùn)含了大量的來(lái)自不同的病人的病理信息,因此借助現(xiàn)代信息科學(xué)技術(shù)來(lái)挖掘醫(yī)生無(wú)法發(fā)現(xiàn)的病理規(guī)律和共性成為了一個(gè)非常有研究?jī)r(jià)值的課題�；趦�(nèi)容的圖像檢索技術(shù)能夠利用現(xiàn)代計(jì)算機(jī)技術(shù)實(shí)現(xiàn)自動(dòng)地在海量圖像中高效、準(zhǔn)確的識(shí)別出與被檢索圖像在視覺(jué)上類似的,具有相同語(yǔ)義的圖像。這一技術(shù)在醫(yī)學(xué)上可以幫助醫(yī)生在數(shù)量巨大的圖像中找到最有價(jià)值的相似醫(yī)學(xué)圖像,通過(guò)發(fā)掘更多的具有同樣影像特征的病例來(lái)為醫(yī)學(xué)臨床診斷、醫(yī)學(xué)教學(xué)和科研提供更為多方面的有價(jià)值的歷史數(shù)據(jù)支持。因此,對(duì)于基于內(nèi)容的醫(yī)學(xué)圖像檢索的研究在臨床診斷和醫(yī)學(xué)研究領(lǐng)域都有重要的意義。本文研究了如下內(nèi)容:1、提出了基于sift局部不變子特征的EVLAD圖像全局特征與基于局部敏感哈希的灰度特征,基于Gabor小波變換算法圖像紋理特征相融合的融合醫(yī)學(xué)圖像特征的提取和計(jì)算方法,實(shí)驗(yàn)證明基于本特征表示法的圖像相似度匹配算法能夠達(dá)到比較理想的精度。2、針對(duì)在醫(yī)生臨床診斷,教學(xué)和科研當(dāng)中常常存在對(duì)特定病灶部位進(jìn)行精準(zhǔn)研究的需要,在本文提出了基于區(qū)域分割的針對(duì)特定病灶區(qū)域的候選集排序算法,通過(guò)醫(yī)生指定的種子點(diǎn)可以對(duì)初步檢索得到的候選集進(jìn)行病灶區(qū)域的分割,并提取病灶區(qū)域特征并依據(jù)該特征對(duì)候選集進(jìn)行重排序,實(shí)現(xiàn)可疑病灶部位的精確檢索和排序。3、提出了聯(lián)合索引結(jié)構(gòu),分別獨(dú)立的對(duì)融合醫(yī)學(xué)特征的三種特征進(jìn)行聚類,并對(duì)不同層的聚類中心進(jìn)行三個(gè)一組的全組合,得到多個(gè)索引節(jié)點(diǎn)。這種索引結(jié)構(gòu)只需計(jì)算少數(shù)聚類中心就可得到多個(gè)索引節(jié)點(diǎn),降低了索引構(gòu)建的計(jì)算量。在索引節(jié)點(diǎn)與圖像進(jìn)行相似度計(jì)算時(shí)引入了權(quán)重計(jì)算,在提高檢索效率的同時(shí)保證了檢索精度。4、根據(jù)醫(yī)學(xué)圖像數(shù)據(jù)量巨大的特點(diǎn),設(shè)計(jì)了一套基于本文檢索算法的分布式計(jì)算架構(gòu),通過(guò)將每個(gè)大型檢索任務(wù)分解為若干個(gè)小任務(wù)在不同的硬件上分布式的并行執(zhí)行,大大提高了算法特征提取、索引構(gòu)建以及在線查詢的運(yùn)行速度,很好的解決了檢索過(guò)程中相關(guān)計(jì)算量巨大的問(wèn)題。
[Abstract]:At present, medical imaging technology has become one of the most reliable assistant technology in medical diagnosis. Every day, hospitals use various imaging techniques such as CTand NMR to generate medical images in different formats and sizes. These massive medical images contain a large amount of pathological information from different patients, so it is a very valuable subject to explore the pathological rules and commonalities that doctors can not find by means of modern information science and technology. Content-Based Image Retrieval (CBIR) technology can use modern computer technology to automatically identify images with the same semantic semantics as the retrieved images in a large amount of images with high efficiency and accuracy. This technique can help doctors find the most valuable medical images in a large number of images, and diagnose the medical clinic by finding more cases with the same image characteristics. Medical teaching and research provide more valuable historical data support. Therefore, the research of content-based medical image retrieval is of great significance in the field of clinical diagnosis and medical research. In this paper, the following content: 1: 1 is studied. The global feature of EVLAD image based on sift local invariant feature and the gray level feature based on locally sensitive hash are proposed. Based on Gabor wavelet transform algorithm, the feature extraction and calculation method of fusion medical image based on image texture feature fusion. Experiments show that the image similarity matching algorithm based on this feature representation method can achieve an ideal accuracy of .2. aiming at the need of precise research on specific lesions in doctors' clinical diagnosis, teaching and scientific research. In this paper, a candidate set sorting algorithm based on region segmentation is proposed. The candidate set can be segmented by using the seed points specified by the doctor. The region feature of the lesion is extracted and the candidate set is reordered according to the feature. The precise retrieval and sorting of the suspected lesions are realized. A joint index structure is proposed to cluster the three features of the fusion medical features independently. The clustering centers of different layers are combined in three groups, and multiple index nodes are obtained. This kind of index structure only needs to calculate a few clustering centers to get multiple index nodes, which reduces the computation amount of index construction. The weight calculation is introduced in the similarity calculation between the index node and the image, which improves the retrieval efficiency and ensures the retrieval accuracy. A distributed computing architecture based on this retrieval algorithm is designed. By decomposing each large retrieval task into several small tasks distributed parallel execution on different hardware, the algorithm feature extraction is greatly improved. The running speed of index construction and online query solves the problem of huge computation in the retrieval process.
【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】：2017
【分類號(hào)】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 顏文;金煒;符冉迪;;結(jié)合VLAD特征和稀疏表示的圖像檢索[J];電信科學(xué);2016年12期

2 梁曄;于劍;劉宏哲;;基于BoF模型的圖像表示方法研究[J];計(jì)算機(jī)科學(xué);2014年02期

3 何云峰;周玲;于俊清;徐濤;管濤;;基于局部特征聚合的圖像檢索方法[J];計(jì)算機(jī)學(xué)報(bào);2011年11期

4 紀(jì)華;吳元昊;孫宏海;王延杰;;結(jié)合全局信息的SIFT特征匹配算法[J];光學(xué)精密工程;2009年02期

5 張春美;龔志輝;孫雷;;改進(jìn)SIFT特征在圖像匹配中的應(yīng)用[J];計(jì)算機(jī)工程與應(yīng)用;2008年02期

6 李卓;邱慧娟;;基于相關(guān)系數(shù)的快速圖像匹配研究[J];北京理工大學(xué)學(xué)報(bào);2007年11期

7 王國(guó)美;陳孝威;;SIFT特征匹配算法研究[J];鹽城工學(xué)院學(xué)報(bào)(自然科學(xué)版);2007年02期

8 趙于前,陳真誠(chéng),李凌云,唐艷;基于柔性數(shù)學(xué)形態(tài)學(xué)的醫(yī)學(xué)圖像邊緣提取[J];計(jì)算機(jī)工程與應(yīng)用;2005年25期

9 汪俊,周來(lái)水,安魯陵,譚昌柏;基于網(wǎng)格模型的一種新的區(qū)域分割算法[J];中國(guó)機(jī)械工程;2005年09期

相關(guān)博士學(xué)位論文 前1條

1 李清亮;圖像檢索中判別性增強(qiáng)研究[D];吉林大學(xué);2016年

相關(guān)碩士學(xué)位論文 前1條

1 唐建鑫;基于Bag of Features的圖像分類和檢索研究[D];西安電子科技大學(xué);2012年

，

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