本文選題：細胞運動分析　切入點：多模式　出處：《南京理工大學》2016年博士論文

【摘要】：生物醫(yī)學圖像在醫(yī)療診斷和疾病治療中的作用日益顯著,細胞圖像的研究是醫(yī)學圖像中一個重要的分支。從細胞圖像序列中提取細胞的特征信息及運動軌跡,是醫(yī)學分析中一項重要的基本工作。近年來,盡管相關(guān)領(lǐng)域的研究者取得了很多有益的成果,但因信息量大,細胞的形狀復雜且易變,細胞運動的隨機性以及受到獲取技術(shù)、圖像質(zhì)量等因素的影響,要準確計量細胞數(shù)目及獲取其運動軌跡,仍然存在許多理論與技術(shù)上的難點。針對以上問題,本論文從分析多細胞存在的不同運動模式著手,充分利用時空信息,提出了若干種具有一定工程應用前景的隨機性多細胞運動分析方法,改進了檢測與跟蹤的準確率。論文的主要研究內(nèi)容如下:1.針對所研究圖像序列中多細胞粘連問題,給出了一種基于閾值的混合細胞檢測算法,仿真結(jié)果表明,基于該圖像序列的查全率(Recall)和準確率(Precision)可達98.32%和97.03%左右。針對密集情形下出現(xiàn)的多細胞重疊問題,給出了一種改進分水嶺混合檢測算法,仿真結(jié)果表明,該方法可以很好地將重疊細胞分割為單個細胞,且?guī)缀醪粫霈F(xiàn)細胞區(qū)域標定不正確或者區(qū)域分割不完整的現(xiàn)象,基于該圖像序列的準確率可達到96%。2.針對多細胞跟蹤中存在的目標分裂、碰撞等引起的狀態(tài)耦合問題,給出了一種擴展多模型粒子濾波多細胞運動分析方法。首先結(jié)合閾值處理和孔洞填充技術(shù)設(shè)計了細胞混合檢測方法。其次基于細胞相互作用存在的三種事件(獨立、碰撞、分裂),構(gòu)建細胞運動模型,并應用角速度和面積特征參數(shù)對目標狀態(tài)進行增廣。最后通過計算面積和距離特征信息的差異性測量,給出了一種細胞數(shù)據(jù)關(guān)聯(lián)策略。仿真結(jié)果表明了設(shè)計方法的有效性。3.針對多細胞數(shù)目時變和動力學特性差異等問題,給出了一種基于圖像背景提取的蟻群多細胞運動分析方法。利用非參數(shù)核密度估計方法產(chǎn)生先驗蟻群分布,通過多蟻群重構(gòu)建立多峰信息素場,利用蟻群快速聚類算法實現(xiàn)細胞身份管理與狀態(tài)提取。仿真結(jié)果表明了設(shè)計方法的有效性。4.針對低信噪比圖像序列中多細胞近鄰問題,給出了一種多任務蟻群近鄰細胞運動分析方法。利用近似平均法方法提取細胞的前景圖像,結(jié)合K均值聚類方法產(chǎn)生初始子蟻群。設(shè)計了蟻群的協(xié)作與競爭模式,并構(gòu)建多峰信息素場。通過合并相似子蟻群和去除虛假目標子蟻群進行多細胞狀態(tài)估計。仿真結(jié)果表明,該方法與其他跟蹤算法相比具有較高的準確性。5.針對低信噪比圖像序列中細胞密度變化問題,給出了一種多模式蟻群變密度細胞運動分析方法。利用當前幀區(qū)域平均似然度,并結(jié)合前一幀細胞動力學特性產(chǎn)生蟻群初始分布。基于細胞的稀疏與密集事件,設(shè)計蟻群的協(xié)作模式及交互競爭模式。利用蟻群之間的交互信息設(shè)計了蟻群工作模式實時更新策略。仿真結(jié)果表明了設(shè)計方法的有效性。
[Abstract]:Biomedical images play an increasingly important role in medical diagnosis and disease treatment. The study of cell images is an important branch of medical images.It is an important work in medical analysis to extract the characteristic information and motion track of cells from cell image sequences.In recent years, although researchers in related fields have made a lot of useful achievements, because of the large amount of information, the complex and changeable shape of cells, the randomness of cell movement and the influence of acquisition technology, image quality and so on,There are still many theoretical and technical difficulties to accurately measure the number of cells and obtain their motion trajectories.In order to solve the above problems, this paper begins with the analysis of the different motion patterns of multicellular, makes full use of space-time information, and puts forward several stochastic multicellular motion analysis methods with a certain engineering application prospect.The accuracy of detection and tracking is improved.The main contents of this thesis are as follows: 1.In order to solve the problem of multi-cell adhesion, a threshold based hybrid cell detection algorithm is proposed. The simulation results show that the recall rate and accuracy rate of the image sequence can reach 98.32% and 97.03% respectively.An improved watershed hybrid detection algorithm is proposed to solve the multi-cell overlap problem in dense case. The simulation results show that the proposed method can divide overlapping cells into single cells.And there is almost no phenomenon that cell region calibration is incorrect or region segmentation is incomplete. The accuracy rate based on the image sequence can reach 96.2.Aiming at the state coupling problems caused by target splitting and collision in multi-cell tracking, an extended multi-model particle filter multi-cell motion analysis method is presented.At first, a method of cell mixed detection was designed by combining threshold processing and hole filling technique.Secondly, based on the three events of cell interaction (independence, collision, division), the model of cell motion is constructed, and the target state is augmented by the parameters of angular velocity and area characteristic.Finally, a cell data association strategy is proposed by measuring the difference between the calculated area and the distance characteristic information.The simulation results show that the design method is effective.In order to solve the problem of time-varying number of multi-cell and difference of dynamic characteristics, a multi-cell motion analysis method of ant colony based on image background extraction is presented.The nonparametric kernel density estimation method is used to generate the prior ant colony distribution, the multi-peak pheromone field is established by the multi-ant colony reconstruction, and the cell identity management and state extraction are realized by the ant colony fast clustering algorithm.Simulation results show the effectiveness of the design method.In order to solve the problem of multi-cell nearest neighbor in low signal-to-noise ratio (SNR) image sequence, a multi-task ant colony nearest neighbor cell motion analysis method is presented.The foreground image of cells was extracted by the approximate averaging method and the initial ant colony was generated by K-means clustering method.The cooperation and competition model of ant colony is designed, and the multi-peak pheromone field is constructed.Multi-cell state estimation was carried out by merging similar ant colonies and removing false target ant colonies.Simulation results show that the method has higher accuracy than other tracking algorithms.In order to solve the problem of cell density change in low SNR image sequence, a multi-mode ant colony variable density cell motion analysis method is presented.The initial distribution of ant colony is generated by using the average likelihood degree of the current frame region and the cellular dynamics of the previous frame.Based on the sparse and dense events of cells, the cooperation mode and the interactive competition mode of ant colony are designed.Based on the interaction information between ant colonies, the real-time updating strategy of ant colony working mode is designed.Simulation results show the effectiveness of the design method.
【學位授予單位】：南京理工大學
【學位級別】：博士
【學位授予年份】：2016
【分類號】：R318;TP391.41

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