【摘要】：數(shù)字病理切片作為遠(yuǎn)程醫(yī)師診斷的重要依據(jù)之一,其精確性直接決定了醫(yī)師判斷的準(zhǔn)確性。由于光學(xué)顯微鏡能夠識(shí)別病理組織結(jié)構(gòu)上的特征,所以光學(xué)顯微鏡成像技術(shù)已經(jīng)被廣泛的用在醫(yī)學(xué)應(yīng)用領(lǐng)域。為了達(dá)到診斷的目的,最終經(jīng)醫(yī)生診斷的病理圖像應(yīng)該是病理組織的整體圖像,并具有較高的分辨率,且能提供較豐富的關(guān)于病理組織的細(xì)節(jié)信息。目前,最新的數(shù)字化的病理掃描系統(tǒng)所能處理的超高分辨率病理組織圖像的像素?cái)?shù)可以超過400M。但是由于常規(guī)圖像采集傳感器的像素?cái)?shù)的限制,我們無法利用常規(guī)的圖像采集陣列實(shí)現(xiàn)一次性掃描如此高像素的圖像。即使是使用超高像素的圖像采集陣列,不僅傳感器價(jià)格昂貴,與其配套使用的光學(xué)系統(tǒng)制作要求也很高,其成本是我們無法承受的。一個(gè)現(xiàn)實(shí)可行的解決方法就是采用當(dāng)前主流技術(shù)的圖像采集系統(tǒng),使用高倍率、小視野,配合步進(jìn)電機(jī)驅(qū)動(dòng)的2D可移動(dòng)精密承載臺(tái),對(duì)一個(gè)病理組織玻片的有效面積內(nèi)的組織進(jìn)行邊界重疊的多次掃描采集,然后利用快速精確的圖像拼接算法把多次采集到的各個(gè)子圖拼接成一個(gè)超高分辨率的完整圖片。基于以上背景,本文的主要工作包含兩部分,一是設(shè)計(jì)了一種光學(xué)顯微鏡下的病理組織圖像采集設(shè)備,二是研究不同的特征點(diǎn)提取算法,改進(jìn)得到一種快速有效的圖像拼接算法來形成整個(gè)病理組織樣本的全景圖像。數(shù)字病理圖像通過我們的顯微鏡圖像采集系統(tǒng)獲得,這些病理圖像只是整個(gè)病理組織的一小部分,并且具有較高的分辨率,除此之外,這些病理組織圖像在物理位置上相鄰,并具有一定大小的重疊區(qū)域,以免丟失拼接時(shí)的邊緣細(xì)節(jié)信息。所有通過圖像采集系統(tǒng)獲取的圖像利用我們改進(jìn)的快速拼接算法來形成整個(gè)病理組織樣本的全景圖。該系統(tǒng)技術(shù)適用于臨床病理研究領(lǐng)域。通過對(duì)我們設(shè)計(jì)的病理圖像采集設(shè)備獲取的圖像,對(duì)改進(jìn)的拼接方法的可行性和表現(xiàn)與現(xiàn)有的方法以及最新的特征點(diǎn)提取算法在處理臨床病理圖像方面進(jìn)行了對(duì)比,我們的方法是高效與精確的,使診斷醫(yī)生可以快速準(zhǔn)確地做出判斷。
[Abstract]:Digital pathological section is one of the important bases for remote diagnosis, and its accuracy directly determines the accuracy of physician's judgment. Optical microscope imaging technology has been widely used in medical applications because of its ability to identify pathological and structural features. In order to achieve the purpose of diagnosis, the final pathological image diagnosed by doctors should be the whole image of pathological tissue, with high resolution, and can provide more detailed information about pathological tissue. At present, the up-to-date digital pathological scanning system can process ultra-high resolution pathological tissue images with a pixel number of more than 400m. However, due to the limitation of the pixel number of the conventional image acquisition sensor, we can not use the conventional image acquisition array to scan such a high pixel image at one time. Even if we use ultra-high pixel image acquisition array, not only the sensor is expensive, but also the requirement of making optical system is very high. The cost of the sensor is too high for us to bear. A practical solution is to use the current mainstream technology of image acquisition system, using high-rate, small field of view, combined with stepping motor driven 2D movable precision bearing table, The tissue in the effective area of a pathological tissue slide is collected by multiple scanning with overlapping boundaries, and then each sub-image collected many times is stitched together into an ultra-high resolution image using a fast and accurate image splicing algorithm. Based on the above background, the main work of this paper consists of two parts: one is to design a kind of image acquisition equipment for pathological tissue under optical microscope, the other is to study different feature point extraction algorithms. A fast and effective image mosaic algorithm is improved to form a panoramic image of the whole pathological tissue sample. Digital pathological images are obtained through our microscope image acquisition system, these pathological images are only a small part of the entire pathological tissue, and have a high resolution, in addition, these pathological tissue images in the physical position adjacent to each other, And has a certain size of overlapping areas, so as to avoid losing the details of the edge of the splicing information. All the images obtained by the image acquisition system use our improved fast stitching algorithm to form a panoramic picture of the whole pathological tissue sample. This system is suitable for clinical pathological research. This paper compares the feasibility and performance of the improved splicing method with the existing methods and the latest feature point extraction algorithm in the processing of clinical pathological images by comparing the images obtained by the pathological image acquisition equipment designed by us, and comparing the feasibility and performance of the improved splicing method with the existing methods and the latest feature point extraction algorithm. Our approach is efficient and accurate, allowing diagnostic doctors to make decisions quickly and accurately.
【學(xué)位授予單位】：山東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R-05;TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張志斌;趙帥領(lǐng);羅錫文;魏鳳岐;;基于SURF算法的綠色作物特征提取與圖像匹配方法[J];農(nóng)業(yè)工程學(xué)報(bào);2015年14期

2 周志艷;閆夢璐;陳盛德;蘭玉彬;羅錫文;;Harris角點(diǎn)自適應(yīng)檢測的水稻低空遙感圖像配準(zhǔn)與拼接算法[J];農(nóng)業(yè)工程學(xué)報(bào);2015年14期

3 胡同喜;牛雪峰;譚洋;陳新鵬;;基于SURF算法的無人機(jī)遙感影像拼接技術(shù)[J];測繪通報(bào);2015年01期

4 張從鵬;魏學(xué)光;;基于Harris角點(diǎn)的矩形檢測[J];光學(xué)精密工程;2014年08期

5 楊世沛;陳杰;周莉;劉健;劉海洋;;一種基于SIFT的圖像特征匹配方法[J];電子測量技術(shù);2014年06期

6 丁雄飛;張春燕;;基于Moravec算子和改進(jìn)的SIFT算法的圖像匹配[J];合肥學(xué)院學(xué)報(bào)(自然科學(xué)版);2013年03期

7 劉佳;傅衛(wèi)平;王雯;李娜;;基于改進(jìn)SIFT算法的圖像匹配[J];儀器儀表學(xué)報(bào);2013年05期

8 仇國慶;馮漢青;蔣天躍;涂樂飛;;一種改進(jìn)的Harris角點(diǎn)圖像拼接算法[J];計(jì)算機(jī)科學(xué);2012年11期

9 陳藝蝦;孫權(quán)森;徐煥宇;耿蕾蕾;;SURF算法和RANSAC算法相結(jié)合的遙感圖像匹配方法[J];計(jì)算機(jī)科學(xué)與探索;2012年09期

10 唐永鶴;陶華敏;盧煥章;胡謀法;;一種基于Harris算子的快速圖像匹配算法[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2012年04期

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