【摘要】：紅細(xì)胞的形態(tài)特征分類與識(shí)別在醫(yī)學(xué)研究上有著重要的意義。目前,許多醫(yī)院和科研院所對(duì)血液紅細(xì)胞的分類與識(shí)別都是采用顯微鏡下人工觀測(cè)細(xì)胞,再對(duì)其形態(tài)進(jìn)行統(tǒng)計(jì)。這種方法只能對(duì)紅細(xì)胞的形態(tài)和數(shù)量等信息做大致的統(tǒng)計(jì)和記錄,如果要精確記錄紅細(xì)胞的面積、形狀、圓形率等信息,人工觀測(cè)的方法很難做到精確而細(xì)致的記錄,同時(shí)對(duì)醫(yī)護(hù)和科研工作者是極大的精力消耗。如果有一套健全的紅細(xì)胞識(shí)別與分類系統(tǒng),能夠?qū)λM(jìn)行全自動(dòng)化處理包括精準(zhǔn)的形態(tài)分析、圖表信息統(tǒng)計(jì)等一系列處理,這將極大的提高紅細(xì)胞的檢測(cè)準(zhǔn)確度和醫(yī)療技術(shù)人員的操作效率。本文從醫(yī)院及科研院所的實(shí)際檢測(cè)需求出發(fā),設(shè)計(jì)并實(shí)現(xiàn)了基于BP神經(jīng)網(wǎng)絡(luò)的紅細(xì)胞識(shí)別與分類系統(tǒng)。具體包括以下工作:1.使用雙邊濾波平滑圖像,采用BM3D算法去除圖像的高斯白噪聲并通過對(duì)圖像進(jìn)行灰度形態(tài)學(xué)操作增強(qiáng)目標(biāo)與背景的對(duì)比度,同時(shí)突出圖像邊緣。預(yù)處理和增強(qiáng)模塊是為了提升圖像品質(zhì),為接下來的分割做鋪墊。2.在先后比較了閾值分割、Canny分割和分水嶺分割等方法之后,根據(jù)對(duì)分割算法的大量無差別測(cè)試,并分析、比較分割結(jié)果,選擇了一種基于標(biāo)記的分水嶺分割算法,這個(gè)算法既能有效地分割紅細(xì)胞,同時(shí)也能保證邊緣特征信息不被大量丟失。分別提取紅細(xì)胞的周長(zhǎng)、面積、圓形度、矩形度及傅里葉形狀描述子等特征,這些都是具有一定區(qū)分度的形狀特征描述。3.用BP神經(jīng)網(wǎng)絡(luò)對(duì)紅細(xì)胞進(jìn)行識(shí)別與分類,對(duì)紅細(xì)胞形狀數(shù)據(jù)進(jìn)行歸一化和交叉驗(yàn)證等操作。最后訓(xùn)練得出一個(gè)識(shí)別率達(dá)到93%以上的BP神經(jīng)網(wǎng)絡(luò)。4.根據(jù)算法完成的任務(wù),將算法處理過程逐一劃分為功能獨(dú)立又相互協(xié)作的功能模塊,同時(shí)保持了算法的可擴(kuò)展性。系統(tǒng)采用三段式界面,這種界面組織方式靈活并且可定制化,非常符合軟件開發(fā)要求。對(duì)系統(tǒng)的功能模塊進(jìn)行了編碼實(shí)現(xiàn)與功能測(cè)試,對(duì)紅細(xì)胞檢測(cè)結(jié)果進(jìn)行圖表化顯示。5.比較了BP神經(jīng)網(wǎng)絡(luò)和決策樹在紅細(xì)胞識(shí)別的準(zhǔn)確率、計(jì)算復(fù)雜度和算法可擴(kuò)展性的優(yōu)劣,得出前者比后者優(yōu)越的結(jié)論。
[Abstract]:The classification and recognition of the morphological characteristics of red blood cells are of great significance in medical research. At present, the classification and recognition of red blood cells in many hospitals and research institutes are observed artificially under microscope, and their morphology is counted. This method can only make general statistics and records of red blood cell shape and quantity. If we want to accurately record the area, shape and roundness of red blood cells, it is very difficult for manual observation methods to record accurately and meticulously. At the same time, the health care and research workers are a great energy consumption. If there is a sound red blood cell recognition and classification system, it can be processed with full automation, including accurate morphological analysis, chart information statistics, and a series of processing. This will greatly improve the detection accuracy of red blood cells and the operational efficiency of medical technicians. In this paper, the recognition and classification system of red blood cells based on BP neural network is designed and realized according to the actual testing requirements of hospitals and scientific research institutes. Include the following work: 1. Using bilateral filtering to smooth the image, BM3D algorithm is used to remove the Gao Si white noise of the image, and the contrast between the target and the background is enhanced by gray-scale morphological operation, and the edge of the image is highlighted at the same time. The preprocessing and enhancement module is designed to improve the image quality and pave the way for the next segmentation. After comparing threshold segmentation with Canny segmentation and watershed segmentation, a watershed segmentation algorithm based on marking is selected according to a large number of undifferentiated tests and analysis of segmentation results. This algorithm can not only effectively segment red blood cells, but also ensure that the edge feature information is not lost. The circumference, area, roundness, rectangle and Fourier shape descriptors of red blood cells were extracted respectively. BP neural network is used to identify and classify red blood cells, and to normalize and cross-verify the shape data of red blood cells. Finally, a BP neural network with a recognition rate of 93% or more was obtained. According to the tasks accomplished by the algorithm, the algorithm processing process is divided into functional independent and cooperative function modules one by one, while maintaining the scalability of the algorithm. The system adopts a three-segment interface, which is flexible and customizable, and meets the requirements of software development. The function module of the system is coded and tested, and the result of red blood cell detection is graphically displayed. 5. The accuracy, computational complexity and expansibility of BP neural network and decision tree in erythrocyte recognition are compared, and the conclusion that the former is superior to the latter is obtained.
【學(xué)位授予單位】：湘潭大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R446.1;TP391.41

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本文編號(hào)：2149677