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

【摘要】：目前在醫(yī)學領(lǐng)域,醫(yī)學成像技術(shù)已經(jīng)成為醫(yī)生進行醫(yī)學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)來生成不同格式、大小的醫(yī)學圖像。這些海量的醫(yī)學圖像蘊含了大量的來自不同的病人的病理信息,因此借助現(xiàn)代信息科學技術(shù)來挖掘醫(yī)生無法發(fā)現(xiàn)的病理規(guī)律和共性成為了一個非常有研究價值的課題�；趦�(nèi)容的圖像檢索技術(shù)能夠利用現(xiàn)代計算機技術(shù)實現(xiàn)自動地在海量圖像中高效、準確的識別出與被檢索圖像在視覺上類似的,具有相同語義的圖像。這一技術(shù)在醫(yī)學上可以幫助醫(yī)生在數(shù)量巨大的圖像中找到最有價值的相似醫(yī)學圖像,通過發(fā)掘更多的具有同樣影像特征的病例來為醫(yī)學臨床診斷、醫(yī)學教學和科研提供更為多方面的有價值的歷史數(shù)據(jù)支持。因此,對于基于內(nèi)容的醫(yī)學圖像檢索的研究在臨床診斷和醫(yī)學研究領(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)學圖像特征的提取和計算方法,實驗證明基于本特征表示法的圖像相似度匹配算法能夠達到比較理想的精度。2、針對在醫(yī)生臨床診斷,教學和科研當中常常存在對特定病灶部位進行精準研究的需要,在本文提出了基于區(qū)域分割的針對特定病灶區(qū)域的候選集排序算法,通過醫(yī)生指定的種子點可以對初步檢索得到的候選集進行病灶區(qū)域的分割,并提取病灶區(qū)域特征并依據(jù)該特征對候選集進行重排序,實現(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),分別獨立的對融合醫(yī)學特征的三種特征進行聚類,并對不同層的聚類中心進行三個一組的全組合,得到多個索引節(jié)點。這種索引結(jié)構(gòu)只需計算少數(shù)聚類中心就可得到多個索引節(jié)點,降低了索引構(gòu)建的計算量。在索引節(jié)點與圖像進行相似度計算時引入了權(quán)重計算,在提高檢索效率的同時保證了檢索精度。4、根據(jù)醫(yī)學圖像數(shù)據(jù)量巨大的特點,設計了一套基于本文檢索算法的分布式計算架構(gòu),通過將每個大型檢索任務分解為若干個小任務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行速度,很好的解決了檢索過程中相關(guān)計算量巨大的問題。
[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.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41

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