果蝇复眼病变诊断系统中光学图像的处理
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摘要
一直以来,提高医学药品的开发速度是研究人员追求的目标。果蝇作为一种优秀的实验动物模型材料,被广泛应用于生物医学与新药研制。通过观察果蝇复眼在施药前后的变化,分析动物活体对药品的适应性。目前这些观察、判断的工作都是由人工完成,工作量大,诊断速度慢,准确率较低。本文研究果蝇复眼病变诊断系统中光学图像的处理,该项工作是光学图像采集技术、数字图像技术以及嵌入式计算机技术应用于医学显微图像领域,尤其是诸如果蝇一类细小生物样本方面的新尝试,并且是一个值得研究和开发商用产品的新方向。
本文在果蝇复眼病变诊断研究方面取得了较有成效的进展,设计实现了基于嵌入式Linux系统的果蝇复眼病变智能检测系统的软件部分。针对果蝇复眼目标与背景的分割问题,提出一种基于色度、色饱和度和亮度(HSI)彩色空间的分割算法。该算法将果蝇复眼RGB图像中R分量代替B分量成RGR图像,并映射到HSI空间在饱和度S分量上分割果蝇复眼;并比较和分析了快速模糊C均值聚类(FFCM)灰度分割法和最大类间方差(OTSU)阈值化分割法在果蝇复眼目标提取上的性能,处理结果表明OTSU法分割果蝇复眼轮廓的优势最为明显。针对OTSU法计算量大,执行效率低的缺点,提出采用鲁棒性和并行性的遗传算法进行优化,并利用改进的遗传算法(IGA)快速的非线性搜索求解最优分割阈值,阐述了改进遗传算法和优化求解的过程,并给出优化实验结果。同时,归纳果蝇复眼的A、B两类病变,利用面积的特征和亮度的特征对复眼病变进行诊断。基于嵌入式Linux系统建立软件开发环境,设计包括基于光学图像采集技术的果蝇复眼显微图像的采集、果蝇复眼的病变诊断、诊断信息的管理以及批量图像的智能诊断等功能模块。最后以QT软件为平台,设计了系统的图形用户界面。
测试结果表明,系统检测精度可达95%以上,响应时间＜0.1s,处理时间＜3s/张,具有友好的人机交互界面,与人工方式相比优势明显,为高效地检测果蝇一类药品检测实验对象提出行之有效的新方法。
To speed up the development of biomedicine has been the goal of the scientific researchers for many years. Drosophila, as a kind of excellent material of experimental animal models, is used extensively in the research of biomedicine and pharmacy. In medical studies, they are usually treated with reagents of interest and the changes in their organs, particularly their compound eyes, are then collected and analyzed. At present, these series of examine processes are still done manually, which makes the research very laborious and inefficient and sometimes affects the accuracy of the results. This paper studies on the optical image processing in the system of drosophila's compound eyes lesion diagnosis. It is a novel attempt to make use of the technologies of optical image capturing, digital image processing and embedded computer to study the kind of medical micro-image domain such as drosophila and mini-type samples. This work will be worth researching and developing merchandise.
It has made some effective improvements in the following two main aspects: the algorithm of drosophila's compound eyes lesion and its application. In that, the design of the software part of the intelligent diagnosis system of drosophila's compound eyes lesion is realized based on embedded Linux system. Here, a novel algorithm is presented for extracting the region of drosophila's compound eyes based on HSI color space. In the algorithm, the color images of drosophila's compound eyes are resolved into three monochrome components (R, G and B), followed by replacing B with R. After mapping from RGB to HSI, the target regions are accurately extracted using OTSU. Two preparing algorithms for the target detection of the drosophila's compound eyes are proposed, which are Fast Fuzzy C-Means (FFCM) clustering algorithm for monochrome image segmentation and maximum class variance (OTSU) thresholding algorithm. After comparing their performance in practice, the results indicate that OTSU thresholding algorithm is better than the other for extracting drosophila's compound eyes on the saturation space. Since of the massive compute of the OTSU thresholding algorithm, improved Genetic Algorithm (IGA), which is robustness, parallelizable and novel, is used to search the best threshold for segmentation. The paper expatiates on IGA principle, its experiment realization and result. In this paper, it uses the characters of area and intensity to diagnose the two different pathological forms, defined as A and B. The environment of software development is established on embedded Linux system, and then the functions are realized, which are the drosophila's compound eyes micro-image collection based on optical image capturing, the multi-format image compression and storage, the compound eyes pathological diagnosis, the diagnosis pictorial information management and the large batch of compound eyes images of intelligent diagnostic, are contained. Finally, based on the QT software as the platform, GUI for automatic detection of pathological changes of drosophila's compound eye is designed.
The text results show that the detection precision of this system has been achieved over 95%, the response time below 0.1s, the processing time below 3s/sheet and a friendly interacting interface. Compared with manual inspection, the intelligent diagnosis system supplies a new effective and better method to test the drug reaction of the subject (such as drosophila) participated the experiment.
本文在果蝇复眼病变诊断研究方面取得了较有成效的进展,设计实现了基于嵌入式Linux系统的果蝇复眼病变智能检测系统的软件部分。针对果蝇复眼目标与背景的分割问题,提出一种基于色度、色饱和度和亮度(HSI)彩色空间的分割算法。该算法将果蝇复眼RGB图像中R分量代替B分量成RGR图像,并映射到HSI空间在饱和度S分量上分割果蝇复眼;并比较和分析了快速模糊C均值聚类(FFCM)灰度分割法和最大类间方差(OTSU)阈值化分割法在果蝇复眼目标提取上的性能,处理结果表明OTSU法分割果蝇复眼轮廓的优势最为明显。针对OTSU法计算量大,执行效率低的缺点,提出采用鲁棒性和并行性的遗传算法进行优化,并利用改进的遗传算法(IGA)快速的非线性搜索求解最优分割阈值,阐述了改进遗传算法和优化求解的过程,并给出优化实验结果。同时,归纳果蝇复眼的A、B两类病变,利用面积的特征和亮度的特征对复眼病变进行诊断。基于嵌入式Linux系统建立软件开发环境,设计包括基于光学图像采集技术的果蝇复眼显微图像的采集、果蝇复眼的病变诊断、诊断信息的管理以及批量图像的智能诊断等功能模块。最后以QT软件为平台,设计了系统的图形用户界面。
测试结果表明,系统检测精度可达95%以上,响应时间＜0.1s,处理时间＜3s/张,具有友好的人机交互界面,与人工方式相比优势明显,为高效地检测果蝇一类药品检测实验对象提出行之有效的新方法。
To speed up the development of biomedicine has been the goal of the scientific researchers for many years. Drosophila, as a kind of excellent material of experimental animal models, is used extensively in the research of biomedicine and pharmacy. In medical studies, they are usually treated with reagents of interest and the changes in their organs, particularly their compound eyes, are then collected and analyzed. At present, these series of examine processes are still done manually, which makes the research very laborious and inefficient and sometimes affects the accuracy of the results. This paper studies on the optical image processing in the system of drosophila's compound eyes lesion diagnosis. It is a novel attempt to make use of the technologies of optical image capturing, digital image processing and embedded computer to study the kind of medical micro-image domain such as drosophila and mini-type samples. This work will be worth researching and developing merchandise.
It has made some effective improvements in the following two main aspects: the algorithm of drosophila's compound eyes lesion and its application. In that, the design of the software part of the intelligent diagnosis system of drosophila's compound eyes lesion is realized based on embedded Linux system. Here, a novel algorithm is presented for extracting the region of drosophila's compound eyes based on HSI color space. In the algorithm, the color images of drosophila's compound eyes are resolved into three monochrome components (R, G and B), followed by replacing B with R. After mapping from RGB to HSI, the target regions are accurately extracted using OTSU. Two preparing algorithms for the target detection of the drosophila's compound eyes are proposed, which are Fast Fuzzy C-Means (FFCM) clustering algorithm for monochrome image segmentation and maximum class variance (OTSU) thresholding algorithm. After comparing their performance in practice, the results indicate that OTSU thresholding algorithm is better than the other for extracting drosophila's compound eyes on the saturation space. Since of the massive compute of the OTSU thresholding algorithm, improved Genetic Algorithm (IGA), which is robustness, parallelizable and novel, is used to search the best threshold for segmentation. The paper expatiates on IGA principle, its experiment realization and result. In this paper, it uses the characters of area and intensity to diagnose the two different pathological forms, defined as A and B. The environment of software development is established on embedded Linux system, and then the functions are realized, which are the drosophila's compound eyes micro-image collection based on optical image capturing, the multi-format image compression and storage, the compound eyes pathological diagnosis, the diagnosis pictorial information management and the large batch of compound eyes images of intelligent diagnostic, are contained. Finally, based on the QT software as the platform, GUI for automatic detection of pathological changes of drosophila's compound eye is designed.
The text results show that the detection precision of this system has been achieved over 95%, the response time below 0.1s, the processing time below 3s/sheet and a friendly interacting interface. Compared with manual inspection, the intelligent diagnosis system supplies a new effective and better method to test the drug reaction of the subject (such as drosophila) participated the experiment.
引文
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[5]暴学祥,李伟红,李霞,等。黑腹果蝇视觉系统显微结构的研究[J].东北示范大学报自然科学版,2004,36(1):71-77。
[6]王明福,佟艳丰,薛万琦.蝇类资源及其利用前景展望[J].资源科学,2004,26(5):153-159.
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