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题名:
嵌入式生物芯片检测系统设计及样点识别算法研究
作者: 严伟
学位类别: 博士
答辩日期: 2007-12-13
授予单位: 中国科学院光电技术研究所
授予地点: 光电技术研究所
导师: 吴钦章
关键词: 生物芯片 ; 样点分割 ; Snake模型 ; ARM ; 嵌入式
其他题名: The Design of the embedded biochip test equipment and the research on spots recognition method
学位专业: 信号与信息处理
中文摘要: 近几年生物芯片技术获得了前所未有的高速发展,新的芯片制备工艺、检测方法等不断涌现,使得高密度微阵列芯片的设计制作成为现实,并且随着生物芯片技术的不断成熟,芯片已经逐渐走出实验室进入我们的生活,这就对该产业链中非常关键的生物芯片检测设备和样点识别方法提出了更高的要求:样点识别算法高效率、高准确率以及检测设备高智能化、自动化。本论文正是基于此,从嵌入式实时处理平台开发和样点分割方法两方面进行了深入的研究与实践。 本文在对生物芯片样点图像进行全面的特性研究的基础上,并结合误差、噪声溯源分析, 得出了复杂背景下样点目标的总体特性,最终建立了一套完全针对于生物芯片样点图像的粗分割方案, 该方案非常适合圆形样点的分割,能实现生物芯片扫描图像的快速定位和分割。 针对生物芯片样点图像存在偏转角度的情况,本文在分析误差来源以及构成原因的基础上,基于重构原理,提出了全自动化的图像微阵列偏转角度计算解决方案。算法针对生物芯片样点阵列空间分布特点,采用了环形投影方法和功率谱估计相结合的功率切片方法,并以此建立起样点阵列的像素位置、偏转角以及功率值的三维分布关系,实现快速斑点阵列偏转角度的精确计算。 此外,由于生物芯片多样性特点,某些芯片样点外形并不一定是简单的孔径问题,本文在比较彻底地解决了样点粗分割及偏转角校正后,提出了局部区域的样点轮廓跟踪方法——Template snake模型,实现由粗到精的宏状多目标分层识别策略,既保证了样点分割的正确率,又加快了执行速度。 在嵌入式实时处理平台方面,本文在简单介绍嵌入式处理器类型、S3C2410嵌入式系统特点以及ARM芯片及操作系统选择原则的基础上,通过对国内外现有基于PC机的生物芯片检测系统局限性分析,确定了以三星公司推出的具有ARM920T内核的芯片S3C2410为 核心处理器,采用ARM+FPGA+CCD的紧耦合互连结构的处理系统作为总体方案,进行了软、硬件平台的开发和集成试验,并详细说明了各软硬件模块的具体实现及优化策略。该平台的建立,实现了原有基于PC系统的检测仪全面升级, 大大增强了仪器的野外现场工作能力及可操作性。
英文摘要: In recent years, an unprecedented progress has been made in biochip technology; high density micro-array chip has been realized due to the new fabrication and measurement technology. Biochip has entered our daily life with the development of its technology, which gave more requirements to the key of biochip industry chains, especially for the test equipments and spot recognization methods. High efficiency and precision of spot recognization method, high intelligence and automation of test equipments can satisfy the critical requirements. Based on the requirements, this paper gave a deep research on real-time embedded platform and sample spot segmentation. Based on the research on the property of sample spot image of biochip, combined with the analyse on the source of errors and noise, general property of sample spot in complex background has been made; finally, a coarse image segmentation method of sample image in biochip has been built. The method specially fitted for the segmentation of circular spots, and the fast locating and segmentation of scanned biochip image can be realized by the method. Due to the deflexion angle of sample spot image in biochip, based on the analyses of error source and its constitute, a automatic algorithm of detecting deflexion angle of micro-arrays has been proposed on the basic of reconstruction theory. The algorithm applied power-sliced method combined with circular projection and power spectrum assumption, so to build the 3-dimension distribution among pixel location, deflexion angle and power value, and the precision deflexion angle of spotted array can be calculated quickly. Besides, because of diversity of biochip image, the shape of some spots can not be uniformly circular. Solved the problem of coarse segmentation and modification of deflexion angle, a method of tracking local spots profile—Template snake model has been proposed in the paper, it can recognize the target from roughness to precision step by step, so to guarantee the precision and efficiency of segmentation. By the introduction of types of embedded processors, characteristics of S3C2410 embedded system, ARM chip and principle of choosing operating system, according to analyzing the limit of detecting system based on PC in foreign country, a general system design based on core processor of S3C2410 (ARM920T kernel, Samsung Inc.) combined with ARM+FPGA+CCD coupled structure has been built. The implementation and optimization of each model were described in detail; by the development and function test of software and hardware, the system was been completely upgraded and has a universal virtue of operation in field compared with the old system based PC.
语种: 中文
内容类型: 学位论文
URI标识: http://ir.ioe.ac.cn/handle/181551/221
Appears in Collections:光电技术研究所博硕士论文_学位论文

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Recommended Citation:
严伟. 嵌入式生物芯片检测系统设计及样点识别算法研究[D]. 光电技术研究所. 中国科学院光电技术研究所. 2007.
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