无线胶囊内窥镜系统及内窥图像中出血智能识别研究
|
摘要
无线胶囊内窥镜（WCE）系统能让临床医生直接观测人体胃肠道（GI）图像，得到病灶体最直观的信息。而且由于其安全方便、无痛无创、全消化道检测的特点，胶囊内窥镜检测技术在临床正得到越来越广泛的应用，其相关技术已成为国内外医疗器械领域的研究热点。作为一种方兴未艾的技术，现有无线胶囊内窥镜系统的图像质量、帧率以及工作时间还不能满足临床应用的需求。特别是胶囊内窥镜在一次检测中产生大量的内窥图像，从大量的内窥图像中寻找病灶信息对医生来说是一件非常费时费力的事情，这也阻碍了无线胶囊内窥镜技术在临床应用中进一步的普及和推广。
针对这些问题，本论文在国家自然科学基金（No：60875061、30570485）、国家高技术研究发展计划（863计划（）No：2006AA04Z368，2007AA04Z234）和上海市科委攻关项目（No：05111021，09DZ1907400）的支持下，对无线胶囊内窥镜系统和内窥图像中出血智能识别技术进行了深入的研究，研制出了两种类型的新型胶囊内窥镜系统样机；提出了颜色向量相似系数的概念，用以定量衡量颜色相似性程度，并推导出了相似系数的计算公式，在此基础上实现了基于颜色向量相似系数的内窥图像出血智能识别算法；使用差异演化算法对概率神经网络进行改进，构造了每个神经元具有自适应的不同的平滑参数的概率神经网络，实现了基于改进概率神经网络的出血智能识别算法。
现有的胶囊内窥镜采用模拟信号来无线传输胃肠道图像，然后在接收盒中再将模拟信号变换回数字图像数据。模拟信号易受干扰，而且两次A/D和D/A转换必然会损失部分信息，所以这种胶囊内窥镜所获得的图像不能满足临床应用的需求。本论文探索了新型胶囊内窥镜系统的工作原理，设计了系统方案和架构，研制了基于JPEG图像格式的数字式无线胶囊内窥镜系统。系统由胶囊内窥镜、体外接收盒、工作站以及应用软件等四部分组成。其中胶囊内窥镜外径为12mm，长28mm，在体内能获得图像分辨率为320240的胃肠道图像。在胶囊内窥镜内，Bayer格式的原始胃肠道图像被压缩为JPEG格式的图像，而且压缩率可以通过控制量化表来改变。压缩后的图像直接被数字式无线通信模块传输出体外，无须转换为模拟信号从而节省了胶囊内窥镜内的D/A和数据接收盒中的A/D两次数模转换。数字图像数据被体外的接收盒接收存储，然后在图像工作站中由图像应用软件进行再现、分析、诊断。与模拟信号的胶囊内窥镜相比，数字信号抗干扰能力强，图像更加清晰，将有助于提高临床上胃肠道疾病的诊断率。
目前的胶囊内窥镜都采用纽扣电池供电，帧率为2f/s，在人体内工作时间为5小时左右。2f/s的帧率不能使临床医生观察消化道器官的运动细节，而且纽扣电池能量有限，限制了胶囊内窥镜的有效工作时间。一方面患有消化道疾病的病人代谢功能弱，胶囊内窥镜前进速度缓慢，经常在未进入小肠之前就由于能量耗尽而停止了工作。另一方面纽扣电池进入人体存在着潜在的威胁。针对这个难题本论文探索了高速视频胶囊内窥镜的工作原理，研究了无线能量传输技术，研制了基于无线供能的NTSC制式视频胶囊内窥镜系统，系统由视频胶囊内窥镜、无线能量传输装置、图像接收盒以及图像工作站四部分组成。其中胶囊内窥镜外径为10mm，长30mm，能将获取的胃肠道图像编制为标准的NTSC制式的视频，然后无线发射出体外，帧率达30f/s。体外的视频接收盒接收NTSC流媒体视频，并编码为MPEGⅡ格式的数字视频文件存储在SD卡中。待检测结束后，视频文件被导入到图像工作站内供医生诊查。无线视频接收盒也可以直接将接收的视频信号传输到图像工作站，由图像工作站的数字采集卡采集并实时地播放显示。所以视频胶囊内窥镜系统可以让医生在病人的检测过程中实时地诊察。无线能量传输系统由体外的无线能量发射装置和内窥镜内部集成的无线能量接收子系统两部分组成。体外的无线能量发射装置由信号发生器、驱动电路以及霍姆赫兹发射线圈组成。霍姆赫兹发射线圈能在其覆盖区域内产生处处均匀变化的磁场。集成在视频胶囊内窥镜内部的能量接收子系统由3维接收线圈与整流稳压电路组成。赫姆赫兹发射线圈和3维接收线圈的配合保证了胶囊内窥镜能在体内任何位置以任意姿态接收到稳定的能量，从而解决了弱耦合无线能量传输技术中能量接收的姿态稳定性和位置稳定性问题。视频胶囊内窥镜由于采用无线能量传输系统提供能量，可以工作任意长的时间，为高速胶囊内窥镜的应用奠定了理论和技术基础。
通过分析内窥图像中出血区域的特征，本文发现：在RGB和HSI颜色空间中，出血像素聚类为特定的模式类，有一个颜色范围将出血像素和非出血像素分开。所以利用颜色特征可以识别出血像素，进而识别出血内窥图像。本文将颜色表示与向量运算相结合，提出了颜色向量相似系数的概念，用以定量度量颜色相似性程度，推导出了颜色向量相似性系数的计算公式。颜色向量相似系数包括色度相似系数和灰度相似系数，当两种颜色越相似则相似系数的值越大，当两种颜色完全相同时，色度相似系数和灰度相似系数同时取最大值1。颜色向量相似系数可以用来定量衡量不同颜色的相似程度，为彩色图像模式识别提供了新的基础工具。在此基础上设计了应用于RGB颜色空间的颜色向量相似系数出血模式分类器，并结合种子区域生长算法实现了内窥图像出血智能识别的新算法。通过实验验证该算法的出血检测灵敏度和特异度分别达97%和90%。相对于已有的算法，该算法识别灵敏度高，而且运算速度快，基本实现了胶囊内窥图像出血智能识别。
基于颜色向量相似系数的出血智能识别算法，能很好地识别内窥图像中的出血，但该方法扩展到其他疾病的病灶智能识别则非常困难。人工神经网络具有良好的自适应和自学习能力，已经在各种模式识别问题中得到了广泛的应用。采用神经网络构建出血识别专家系统是解决胶囊内窥图像中出血智能识别的理想方式，并能为后续的病灶识别专家系统打下基础。本文在RGB和HSI颜色空间中提取了像素的颜色特征，以像素的6维颜色特征向量为输入，构建了BP神经网络的出血模式分类器，并通过软件编程实现了该出血智能识别算法。通过实验测试，该BP神经网络出血智能识别方法的出血识别灵敏度和特异度分别为93%和96%。但该方法识别速度慢，不适合大量图像的识别。概率神经网络具有训练时问短，结构稳定，能产生贝叶斯后验概率输出的特点，因此具有强大的非线性识别能力，特别适合识别问题。但基本的概率神经网络由于每个神经元采用相同的平滑参数σ，因此识别率较低。本文使用差异演化算法对基本的概率神经网络进行了改进，使每个神经元具有自适应的不同的平滑参数，提高了概率神经网络的识别效率。与BP神经网络类似，使用像素颜色特征向量作为输入，构建了基于改进的概率神经网络的出血模式分类器，并通过软件编程实现了这种出血智能识别算法。实验显示该概率神经网络出血智能识别算法的出血识别灵敏度为93.1%，特异度为85.8%。相对于已有的出血识别算法，该方法灵敏度高，识别速度快，而且结构稳定，重复性好，基本实现了胶囊内窥图像中出血的智能识别，将应用于胶囊内窥图像的初步检测，并为实现其他疾病病灶的智能识别打下了良好的基础。
Wireless Capsule Endoscopy (WCE) system can directly image the humangastrointestinal (GI) tract, and allows clinicians to directly view the lesions anddiagnose the GI tract diseases noninvasively. The usage of WCE is convenientand painless, and the entire GI tract is examined without a dead zone. TheWCE is applied more and more in clinical examinations based on these assets,and the research on technologies concerning WCE has become popular in themedical device industry at home and abroad. As a novel technology, theexisting WCE systems can not yet satisfy the clinical demands, because of thelow quality image, low frame rate and limited working time. In the meaningtime, WCE will generate a large number of images in one examination of apatient. It is, therefore, very laborious and time-consuming to review the WCEimages and hard to find the lesions of diseases, thus limiting the application ofWCE.
Aimed at these problems, with the supports from the National HighTechnology Research and Development Program of China（863Program）, theNational Natural Science Foundation of China and the Science and TechnologyCommission of Shanghai Municipality, this thesis has carried out deeplyresearch on the technologies of WCE system and the intelligent bleedingdetection from WCE images. Two kindsof WCE systems have been developedin this thesis. The concept of color vector similarity coefficient has beenproposed to quantitatively measure the similarity degree of different colors, andthe calculation formula has been deduced. And then a new bleeding detectionfrom WCE images based on the color vector similarity coefficient has beenimplemented. Using differential evolution (DE) to improve the basicprobabilistic neural networks (PNN), a new PNN with different smoothingparameters in each neuron has been built, and then the intelligent bleedingdetection based on the improved PNN has been implemented.
In the existing WCE systems the GI tract images are transmitted withanalog signal, and then the analog signal is converted back into digital imagesin the receiving box. The analog signal is susceptible to interference, inaddition that some information has to be lost during the two times of A/D andD/A conversion. As a consequence, the WCE images can not yet satisfy thedemands of clinical diagnose. A novel JPEG-based digital WCE system hasbeen developed in this thesis, which is constituted of JPEG-based capsule endoscope, image receiving box, image working station and the applicationsoftware. The JPEG-based capsule endoscope is12mm in length and28mm indiameter, which is small enough to be swallowed for patients. After beingswallowed, the capsule endoscope travels through the entire GI tract withnatural peristalses. During the course the capsule endoscope has the ability tocapture GI tract images with the resolution of320240, and the ability tocompress the Bayer images into JPEG image format, and the compression ratecan be adjusted through the quantization table. The compressed JPEG imagesare directly transmitted out wirelessly and digitally, and so two times of D/Aand A/D conversion are saved. The digital JPEG images are received and storedby the outer receiving box which is tied to the patient’s waist. Afterexamination, the images are downloaded into the image workstation for playand diagnosis. Compared with the existing WCE systems with analog signal,digital signal is stronger against the interference, and so the images are clear,which will improve the diagnosis rate.
The existing capsule endoscope is powered by a cell button, which can keepworking continuously for5hours, and the frame rate is about2f/s. The limitedworking time and low image frame rate limit the wider application. Because2f/s is obviously not enough for diagnose the details of GI tract. Furthermore,for a patient with GI tract disease, WCE travels slowly and it needs greatlymore than8hours to diagnose the entire GI tract, as a consequence, the cell button often runs out of power while the small intestine is still not examined. Inthe meaning time, the cell button in human body is a kind of underlying threat.A novel NTSC video WCE system based on wireless power supply isdeveloped in this thesis which is constituted of video capsule endoscope,wireless electric power transmitting device, image receiving box and imagework station. The capsule endoscope is10mm in diameter and30mm in length,and has the ability to capture GI tract images in human body, and the ability tocode the images into NTSC video with the frame rate of30f/s. The video signalis transmitted out of patient's body wirelessly and received by the imagereceiving box. In the receiving box, the NTSC video is encoded into MPEGⅡformat digital video file and saved in it. After the examination being finished,the digital video file is downloaded into the image work station to be played bythe special application software and diagnosed by the clinician. The receivingbox also has the ability to deliver the NTSC video signal directly onto theworkstation, and NTSC video is played real-time. So the clinician cansupervise the examination and diagnose real-time. The wireless power supplysystem is constituted of the outside wireless power transmitting device and theinner wireless power receiver subsystem embedded in the capsule endoscope.The outside wireless power transmitting device is constituted of signalgenerator, driver circuit model and a pair of Helmholtz transmitting coils (TC).Helmholtz TC can generate uniformed changing magnetic field in the special space. The wireless power receiving subsystem is constituted of a3dimensional receiving coil (RC) and a rectifier voltage regulator. Thecollaboration between Helmhoz TC and3D RC guarantees the video capsuleendoscope can receive electric power at any posture anywhere, and theproblems of position and posture stabilities are resolved. Because the videocapsule endoscope is powered wirelessly, the video WCE system can work aslong as needed, which is a real long working time and high speed WCE system.This research work lays a theoretical and technological foundation for theapplication of high speed video WCE system.
The bleeding regions in WCE images are analyzed and the features areexacted in this thesis. Bleeding pixels and non-bleeding pixels are grouped intodifferent pattern classes, and there is a certain color region separating thebleeding pattern class from non-bleeding pattern class. Then the color featurescan be used to recognize the bleeding pixels and then recognize the bleedingWCE images. Combining the color representation with the vector calculation,this thesis proposes the color vector similarity coefficients to quantitativelymeasure the similarity degree between different colors, and deduces thecalculation formulas of the similarity coefficients. The color vector similaritycoefficients include chroma similarity coefficient and gray intensity similaritycoefficient. More similar the colors are, bigger the coefficients are, and whentwo color are exact same, both chroma and gray intensity similarity coefficients get their maxima value1. The chroma and gray intensity similarity coefficientscan be used to quantitatively measure the similarity degree between differentcolors, and are great tools for color image processing. The bleeding patternclassifier based on the color vector similarity coefficient is built in this thesis,and combining the seed region growing, the intelligent bleeding detectionalgorithm based on the color vector similarity coefficient is implemented. Theexperiments are designed to test the bleeding detection algorithm, which ismeasured to have the sensitivity of97%and the specificity of90%. Theexperiments show that the algorithm is featured as fast calculation and highsensitivity.
Artificial neural network (ANN) is featured as good abilities of adaptiveand self-learning, and is widely applied in variety of pattern recognitionproblems. The bleeding detection expert system (ES) based on ANN is an idealsolution for the bleeding detection in WCE images, and can bring good basisfor the disease lesions detection. The features of bleeding region in WCEimages are extracted in RGB and HSI color spaces to form the feature vectors.Using the feature vectors as the input, a BP neural networks classifier is built,based on which, the intelligent bleeding detection algorithm is implementedthrough programming. The experiment show that the sensitivity and specificityof this bleeding detection algorithm is93%and96%respectively. Probabilisticneural network (PNN) is a feedforward neural network based on the radial basis function and the Bayesian theory. It combines some of the best attributes ofstatistical pattern recognition and feedforward artificial neural networks, andcan produce outputs with Bayesian posterior probabilities. It is also characteredas fast learning and stability, and so is very competent for the nonlinear patternrecognition. Nevertheless basic PNN has same smoothing parameters σ inevery neuron, and so the recognition rate is low. Using the differentialevolution (DE) algorithm, this thesis has improved the basis PNN to holddifferent smoothing parameters σ in each neuron, so the recognition rate isimproved. Using the feature vector of color pixel in HSI and RGB color spaceas the input, the bleeding pattern classifier is built based on the improved PNN,and the intelligent bleeding detection algorithm based on improved PNN isimplemented. The experiment shows that the sensitivity of this algorithm is93.1%and the specificity is85.8%. Compared with BP bleeding detectionalgorithm, the improved PNN bleeding detection algorithm is charactered asfast recognition and stable architecture. The automatic and intelligent bleedingdetection is implemented and will be used in the clinical filed to process theWCE images primarily, and lays the foundation for the intelligent detections ofother kinds of lesions.
针对这些问题，本论文在国家自然科学基金（No：60875061、30570485）、国家高技术研究发展计划（863计划（）No：2006AA04Z368，2007AA04Z234）和上海市科委攻关项目（No：05111021，09DZ1907400）的支持下，对无线胶囊内窥镜系统和内窥图像中出血智能识别技术进行了深入的研究，研制出了两种类型的新型胶囊内窥镜系统样机；提出了颜色向量相似系数的概念，用以定量衡量颜色相似性程度，并推导出了相似系数的计算公式，在此基础上实现了基于颜色向量相似系数的内窥图像出血智能识别算法；使用差异演化算法对概率神经网络进行改进，构造了每个神经元具有自适应的不同的平滑参数的概率神经网络，实现了基于改进概率神经网络的出血智能识别算法。
现有的胶囊内窥镜采用模拟信号来无线传输胃肠道图像，然后在接收盒中再将模拟信号变换回数字图像数据。模拟信号易受干扰，而且两次A/D和D/A转换必然会损失部分信息，所以这种胶囊内窥镜所获得的图像不能满足临床应用的需求。本论文探索了新型胶囊内窥镜系统的工作原理，设计了系统方案和架构，研制了基于JPEG图像格式的数字式无线胶囊内窥镜系统。系统由胶囊内窥镜、体外接收盒、工作站以及应用软件等四部分组成。其中胶囊内窥镜外径为12mm，长28mm，在体内能获得图像分辨率为320240的胃肠道图像。在胶囊内窥镜内，Bayer格式的原始胃肠道图像被压缩为JPEG格式的图像，而且压缩率可以通过控制量化表来改变。压缩后的图像直接被数字式无线通信模块传输出体外，无须转换为模拟信号从而节省了胶囊内窥镜内的D/A和数据接收盒中的A/D两次数模转换。数字图像数据被体外的接收盒接收存储，然后在图像工作站中由图像应用软件进行再现、分析、诊断。与模拟信号的胶囊内窥镜相比，数字信号抗干扰能力强，图像更加清晰，将有助于提高临床上胃肠道疾病的诊断率。
目前的胶囊内窥镜都采用纽扣电池供电，帧率为2f/s，在人体内工作时间为5小时左右。2f/s的帧率不能使临床医生观察消化道器官的运动细节，而且纽扣电池能量有限，限制了胶囊内窥镜的有效工作时间。一方面患有消化道疾病的病人代谢功能弱，胶囊内窥镜前进速度缓慢，经常在未进入小肠之前就由于能量耗尽而停止了工作。另一方面纽扣电池进入人体存在着潜在的威胁。针对这个难题本论文探索了高速视频胶囊内窥镜的工作原理，研究了无线能量传输技术，研制了基于无线供能的NTSC制式视频胶囊内窥镜系统，系统由视频胶囊内窥镜、无线能量传输装置、图像接收盒以及图像工作站四部分组成。其中胶囊内窥镜外径为10mm，长30mm，能将获取的胃肠道图像编制为标准的NTSC制式的视频，然后无线发射出体外，帧率达30f/s。体外的视频接收盒接收NTSC流媒体视频，并编码为MPEGⅡ格式的数字视频文件存储在SD卡中。待检测结束后，视频文件被导入到图像工作站内供医生诊查。无线视频接收盒也可以直接将接收的视频信号传输到图像工作站，由图像工作站的数字采集卡采集并实时地播放显示。所以视频胶囊内窥镜系统可以让医生在病人的检测过程中实时地诊察。无线能量传输系统由体外的无线能量发射装置和内窥镜内部集成的无线能量接收子系统两部分组成。体外的无线能量发射装置由信号发生器、驱动电路以及霍姆赫兹发射线圈组成。霍姆赫兹发射线圈能在其覆盖区域内产生处处均匀变化的磁场。集成在视频胶囊内窥镜内部的能量接收子系统由3维接收线圈与整流稳压电路组成。赫姆赫兹发射线圈和3维接收线圈的配合保证了胶囊内窥镜能在体内任何位置以任意姿态接收到稳定的能量，从而解决了弱耦合无线能量传输技术中能量接收的姿态稳定性和位置稳定性问题。视频胶囊内窥镜由于采用无线能量传输系统提供能量，可以工作任意长的时间，为高速胶囊内窥镜的应用奠定了理论和技术基础。
通过分析内窥图像中出血区域的特征，本文发现：在RGB和HSI颜色空间中，出血像素聚类为特定的模式类，有一个颜色范围将出血像素和非出血像素分开。所以利用颜色特征可以识别出血像素，进而识别出血内窥图像。本文将颜色表示与向量运算相结合，提出了颜色向量相似系数的概念，用以定量度量颜色相似性程度，推导出了颜色向量相似性系数的计算公式。颜色向量相似系数包括色度相似系数和灰度相似系数，当两种颜色越相似则相似系数的值越大，当两种颜色完全相同时，色度相似系数和灰度相似系数同时取最大值1。颜色向量相似系数可以用来定量衡量不同颜色的相似程度，为彩色图像模式识别提供了新的基础工具。在此基础上设计了应用于RGB颜色空间的颜色向量相似系数出血模式分类器，并结合种子区域生长算法实现了内窥图像出血智能识别的新算法。通过实验验证该算法的出血检测灵敏度和特异度分别达97%和90%。相对于已有的算法，该算法识别灵敏度高，而且运算速度快，基本实现了胶囊内窥图像出血智能识别。
基于颜色向量相似系数的出血智能识别算法，能很好地识别内窥图像中的出血，但该方法扩展到其他疾病的病灶智能识别则非常困难。人工神经网络具有良好的自适应和自学习能力，已经在各种模式识别问题中得到了广泛的应用。采用神经网络构建出血识别专家系统是解决胶囊内窥图像中出血智能识别的理想方式，并能为后续的病灶识别专家系统打下基础。本文在RGB和HSI颜色空间中提取了像素的颜色特征，以像素的6维颜色特征向量为输入，构建了BP神经网络的出血模式分类器，并通过软件编程实现了该出血智能识别算法。通过实验测试，该BP神经网络出血智能识别方法的出血识别灵敏度和特异度分别为93%和96%。但该方法识别速度慢，不适合大量图像的识别。概率神经网络具有训练时问短，结构稳定，能产生贝叶斯后验概率输出的特点，因此具有强大的非线性识别能力，特别适合识别问题。但基本的概率神经网络由于每个神经元采用相同的平滑参数σ，因此识别率较低。本文使用差异演化算法对基本的概率神经网络进行了改进，使每个神经元具有自适应的不同的平滑参数，提高了概率神经网络的识别效率。与BP神经网络类似，使用像素颜色特征向量作为输入，构建了基于改进的概率神经网络的出血模式分类器，并通过软件编程实现了这种出血智能识别算法。实验显示该概率神经网络出血智能识别算法的出血识别灵敏度为93.1%，特异度为85.8%。相对于已有的出血识别算法，该方法灵敏度高，识别速度快，而且结构稳定，重复性好，基本实现了胶囊内窥图像中出血的智能识别，将应用于胶囊内窥图像的初步检测，并为实现其他疾病病灶的智能识别打下了良好的基础。
Wireless Capsule Endoscopy (WCE) system can directly image the humangastrointestinal (GI) tract, and allows clinicians to directly view the lesions anddiagnose the GI tract diseases noninvasively. The usage of WCE is convenientand painless, and the entire GI tract is examined without a dead zone. TheWCE is applied more and more in clinical examinations based on these assets,and the research on technologies concerning WCE has become popular in themedical device industry at home and abroad. As a novel technology, theexisting WCE systems can not yet satisfy the clinical demands, because of thelow quality image, low frame rate and limited working time. In the meaningtime, WCE will generate a large number of images in one examination of apatient. It is, therefore, very laborious and time-consuming to review the WCEimages and hard to find the lesions of diseases, thus limiting the application ofWCE.
Aimed at these problems, with the supports from the National HighTechnology Research and Development Program of China（863Program）, theNational Natural Science Foundation of China and the Science and TechnologyCommission of Shanghai Municipality, this thesis has carried out deeplyresearch on the technologies of WCE system and the intelligent bleedingdetection from WCE images. Two kindsof WCE systems have been developedin this thesis. The concept of color vector similarity coefficient has beenproposed to quantitatively measure the similarity degree of different colors, andthe calculation formula has been deduced. And then a new bleeding detectionfrom WCE images based on the color vector similarity coefficient has beenimplemented. Using differential evolution (DE) to improve the basicprobabilistic neural networks (PNN), a new PNN with different smoothingparameters in each neuron has been built, and then the intelligent bleedingdetection based on the improved PNN has been implemented.
In the existing WCE systems the GI tract images are transmitted withanalog signal, and then the analog signal is converted back into digital imagesin the receiving box. The analog signal is susceptible to interference, inaddition that some information has to be lost during the two times of A/D andD/A conversion. As a consequence, the WCE images can not yet satisfy thedemands of clinical diagnose. A novel JPEG-based digital WCE system hasbeen developed in this thesis, which is constituted of JPEG-based capsule endoscope, image receiving box, image working station and the applicationsoftware. The JPEG-based capsule endoscope is12mm in length and28mm indiameter, which is small enough to be swallowed for patients. After beingswallowed, the capsule endoscope travels through the entire GI tract withnatural peristalses. During the course the capsule endoscope has the ability tocapture GI tract images with the resolution of320240, and the ability tocompress the Bayer images into JPEG image format, and the compression ratecan be adjusted through the quantization table. The compressed JPEG imagesare directly transmitted out wirelessly and digitally, and so two times of D/Aand A/D conversion are saved. The digital JPEG images are received and storedby the outer receiving box which is tied to the patient’s waist. Afterexamination, the images are downloaded into the image workstation for playand diagnosis. Compared with the existing WCE systems with analog signal,digital signal is stronger against the interference, and so the images are clear,which will improve the diagnosis rate.
The existing capsule endoscope is powered by a cell button, which can keepworking continuously for5hours, and the frame rate is about2f/s. The limitedworking time and low image frame rate limit the wider application. Because2f/s is obviously not enough for diagnose the details of GI tract. Furthermore,for a patient with GI tract disease, WCE travels slowly and it needs greatlymore than8hours to diagnose the entire GI tract, as a consequence, the cell button often runs out of power while the small intestine is still not examined. Inthe meaning time, the cell button in human body is a kind of underlying threat.A novel NTSC video WCE system based on wireless power supply isdeveloped in this thesis which is constituted of video capsule endoscope,wireless electric power transmitting device, image receiving box and imagework station. The capsule endoscope is10mm in diameter and30mm in length,and has the ability to capture GI tract images in human body, and the ability tocode the images into NTSC video with the frame rate of30f/s. The video signalis transmitted out of patient's body wirelessly and received by the imagereceiving box. In the receiving box, the NTSC video is encoded into MPEGⅡformat digital video file and saved in it. After the examination being finished,the digital video file is downloaded into the image work station to be played bythe special application software and diagnosed by the clinician. The receivingbox also has the ability to deliver the NTSC video signal directly onto theworkstation, and NTSC video is played real-time. So the clinician cansupervise the examination and diagnose real-time. The wireless power supplysystem is constituted of the outside wireless power transmitting device and theinner wireless power receiver subsystem embedded in the capsule endoscope.The outside wireless power transmitting device is constituted of signalgenerator, driver circuit model and a pair of Helmholtz transmitting coils (TC).Helmholtz TC can generate uniformed changing magnetic field in the special space. The wireless power receiving subsystem is constituted of a3dimensional receiving coil (RC) and a rectifier voltage regulator. Thecollaboration between Helmhoz TC and3D RC guarantees the video capsuleendoscope can receive electric power at any posture anywhere, and theproblems of position and posture stabilities are resolved. Because the videocapsule endoscope is powered wirelessly, the video WCE system can work aslong as needed, which is a real long working time and high speed WCE system.This research work lays a theoretical and technological foundation for theapplication of high speed video WCE system.
The bleeding regions in WCE images are analyzed and the features areexacted in this thesis. Bleeding pixels and non-bleeding pixels are grouped intodifferent pattern classes, and there is a certain color region separating thebleeding pattern class from non-bleeding pattern class. Then the color featurescan be used to recognize the bleeding pixels and then recognize the bleedingWCE images. Combining the color representation with the vector calculation,this thesis proposes the color vector similarity coefficients to quantitativelymeasure the similarity degree between different colors, and deduces thecalculation formulas of the similarity coefficients. The color vector similaritycoefficients include chroma similarity coefficient and gray intensity similaritycoefficient. More similar the colors are, bigger the coefficients are, and whentwo color are exact same, both chroma and gray intensity similarity coefficients get their maxima value1. The chroma and gray intensity similarity coefficientscan be used to quantitatively measure the similarity degree between differentcolors, and are great tools for color image processing. The bleeding patternclassifier based on the color vector similarity coefficient is built in this thesis,and combining the seed region growing, the intelligent bleeding detectionalgorithm based on the color vector similarity coefficient is implemented. Theexperiments are designed to test the bleeding detection algorithm, which ismeasured to have the sensitivity of97%and the specificity of90%. Theexperiments show that the algorithm is featured as fast calculation and highsensitivity.
Artificial neural network (ANN) is featured as good abilities of adaptiveand self-learning, and is widely applied in variety of pattern recognitionproblems. The bleeding detection expert system (ES) based on ANN is an idealsolution for the bleeding detection in WCE images, and can bring good basisfor the disease lesions detection. The features of bleeding region in WCEimages are extracted in RGB and HSI color spaces to form the feature vectors.Using the feature vectors as the input, a BP neural networks classifier is built,based on which, the intelligent bleeding detection algorithm is implementedthrough programming. The experiment show that the sensitivity and specificityof this bleeding detection algorithm is93%and96%respectively. Probabilisticneural network (PNN) is a feedforward neural network based on the radial basis function and the Bayesian theory. It combines some of the best attributes ofstatistical pattern recognition and feedforward artificial neural networks, andcan produce outputs with Bayesian posterior probabilities. It is also characteredas fast learning and stability, and so is very competent for the nonlinear patternrecognition. Nevertheless basic PNN has same smoothing parameters σ inevery neuron, and so the recognition rate is low. Using the differentialevolution (DE) algorithm, this thesis has improved the basis PNN to holddifferent smoothing parameters σ in each neuron, so the recognition rate isimproved. Using the feature vector of color pixel in HSI and RGB color spaceas the input, the bleeding pattern classifier is built based on the improved PNN,and the intelligent bleeding detection algorithm based on improved PNN isimplemented. The experiment shows that the sensitivity of this algorithm is93.1%and the specificity is85.8%. Compared with BP bleeding detectionalgorithm, the improved PNN bleeding detection algorithm is charactered asfast recognition and stable architecture. The automatic and intelligent bleedingdetection is implemented and will be used in the clinical filed to process theWCE images primarily, and lays the foundation for the intelligent detections ofother kinds of lesions.
引文
[1] Hospital Queen Elizabeth, Highlights on Cancer Statistics2000, in the Hong Kong Cancer Registry.2003: Hong Kong, China:5-8.
[2] Mc Caffrey C., Chevalerias O., O'Mathuna C., et al., Swallowable-capsule technology, IEEEPervasive Computing,2008.7(1):23-29.
[3] Clearinghouse National digestive diseases information, Bleeding in the Digestive Tract. Vol.07.2004,Bethesda, Maryland: National Institutes of Health
[4] Lewis Blair S., SMALL INTESTINAL BLEEDING, Gastroenterology Clinics of North America,2000.29(1):67-95.
[5] Zuckerman Gary R., Prakash Chandra, Askin Matthew P., et al., AGA technical review on theevaluation and management of occult and obscure gastrointestinal bleeding, Gastroenterology,2000.118(1):201-221.
[6] M. HOWARTH D., K. TANG and W. LEES, The clinical utility of nuclear medicine imaging for thedetection of occult gastrointestinal haemorrhage, Nucl Med Commun,2002.23(6):591-594.
[7] Zworykin V.K., A`Radio Pill', Nature,1957.179(4566):898.
[8] MACKAY R. STUART and JACOBSON BERTIL, Endoradiosonde, Nature,1957.179(4572):1239-1240.
[9] J. Fallingborg, Intraluminal pH of the human gastrointestinal tract, Danish medical bulletin,1999.46(3):183-96.
[10] Eliakim R., Suissa A., Yassin K., et al., Wireless capsule video endoscopy compared to bariumfollow-through and computerised tomography in patients with suspected Crohn's disease--final report,Digestive and Liver Disease,2004.36(8):519-522.
[11]郑小林,无线胶囊式小肠内窥镜技术进展,中国医疗器械信息,2004.10(4):6-8.
[12] Bourbakis N., Makrogiannis S. and Kavraki D. A neural network-based detection of bleeding insequences of WCE images.5th IEEE Symposium on Bioinformatics and Bioengineering.2005.Minneapolis, MN: Ieee Computer Soc:324-327.
[13] Iddan Gavriel, Meron Gavriel, Glukhovsky Arkady, et al., Wireless capsule endoscopy, Nature,2000.405(6785):417-417.
[14] Swain P., Iddan G., Meron G., et al., Wireless capsule endoscopy of the small-bowel. Development,testing and first human trials, Biomonitoring and Endoscopy Technologies,2001.1(29):19-23.
[15] Meron G. D., The development of the swallowable video capsule (M2A), Gastrointestinal Endoscopy,2000.52(6):817-819.
[16] Glukhovsky A., Wireless capsule endoscopy, Sensor Review,2003.23(2):128-33.
[17] Co. Given Imaging. PillCam ESO2.2007[cited2007June14]; Available from:http://www.medgadget.com/archives/2007/06/pillcam_eso_2.html.
[18] Co. Given Imaging. The Third Generation PillCam SB Launched.2005[cited2005October13];Available from: http://medgadget.com/archives/2005/10/the_third_gener.html.
[19] Co. Olympus Optical, Medical Camera System, Office P.a.T., Editor.1981: USA
[20] Co. Olympus, Olympus Launches High-resolution Capsule Endoscope in Europe.2008
[21] Co. Olympus Optical, Capsule Endoscope, office P.a.T., Editor.2007: USA
[22] Lab RF. System. The Next Generation Capsule Endoscope.2008March,15,2008[cited200811st,Oct.]; Available from: http://www.rfamerica.com/sayaka/index.html.
[23] Park H.J., Nam H.W., Song B.S., et al. Design of bi-directional and multi-channel miniaturizedtelemetrymodule for wireless endoscopy.2nd Annual International IEEE-EMB Special TopicConference on Microtechnologies in Medicine&Biology.2002. Madison, WI, USA:273-276.
[24] S Bang, J.Y. Park, S. Jeong, et al., First clinical trial of the "MiRo" capsule endoscope by using anovel transmission technology: electric-field propagation, Gastrointestinal Endoscopy,2009.69(2):253-259.
[25]吴复刚,张智高and贾爱芹, OMOM胶囊内镜检查疑诊小肠出血37例分析,中国实用医药,2009.9:75-76.
[26]金安琴,黄晓俊and刘子燕, OMOM胶囊内镜对小肠疾病的诊断价值初步研究,临床消化病杂志,2009.21(1):28-30.
[27] LI Chen-yi, ZHANG Bing-ling, CHEN Chun-xiao, et al., OMOM capsule endoscopy in diagnosis ofsmall bowel disease, Journal of Zhejiang University(Science B),2008.9(11):857-862.
[28]马莹,马彩虹and谭玉娥,国产OMOM胶囊内镜临床应用分析,延安大学学报(医学科学版),2008.24(5):388-389.
[29]谢翔,李国林,张春, et al.,一种双向、数字式微型无线内窥镜系统设计,固体电子学研究与进展,2007(27):123-129.
[30] Zhihua Wang, Xiang Xie, Chun Zhang, et al. IC design for a digital wireless endoscope capsulesystem.Radio Science Conference.2004:K42-K43.
[31]谢翔,李国林,池保勇, et al.,双向、数字化的微型无线内窥镜胶囊内数模混合电路设计,清华大学学报（自然科学版）,2005.45(10):1368-1372.
[32]陈新凯,谢翔,秦豫, et al.,无线内窥镜专用芯片的FPGA验证及相关测试,半导体技术,2005.30(9):46-49.
[33] Chi Baoyong, Yao Jinke, Han Shuguang, et al., Low power high data rate wireless endoscopytransceiver, Microelectronics Journal.38(10-11):1070-1081.
[34]简小云,梅涛and汪小华,胶囊内窥镜机器人的外磁场驱动方法,机器人,2005.27(4):367-372.
[35]付国强,梅涛and孔德义,无线微型机器人肠道内窥镜系统中图像采集与无线传输子系统的设计,光学精密工程,2002.10(6):614-618.
[36]王辉静and简小云,体内医疗微型机器人的外磁场驱动与控制系统研究,深圳信息职业技术学院学报,2006.4(4):10-13.
[37] Appleyard M., Glukhovsky A. and Swain P., Wireless-capsule diagnostic endoscopy for recurrentsmall-bowel bleeding, New England Journal of Medicine,2001.344(3):232-233.
[38] Storch Ian M., Schmelkin Ira, Vance Shirley, et al., A Double Blind Placebo Controlled Study ofTegaserod to Increase Gastric and Small Bowel Transit Times with Wireless Capsule Endoscocpy(WCE): Preliminary Data, Gastrointestinal Endoscopy,2006.63(5):AB192-AB192.
[39] Sturniolo Giacomo C., Di Leo Vincenza, Vettorato Maria G., et al., Small Bowel Exploration byWireless Capsule Endoscopy: Results from314Procedures, American Journal of Medicine,2006.119(4):341-347.
[40] Canlas Karen R, Dobozi Brian M, Lin Sauyu, et al., Using capsule endoscopy to identify GI tractlesions in cirrhotic patients with portal hypertension and chronic anemia, Journal of ClinicalGastroenterology,2008.42(7):844-848.
[41] Carretero C., Fernandez-Urien I., Betes M., et al., Role of videocapsule endoscopy for gastrointestinalbleeding, World Journal of Gastroenterology,2008.14(34):5261-5264.
[42] Cellier C., Obscure gastrointestinal bleeding: Role of videocapsule and double-balloon enteroscopy,Best Practice&Research in Clinical Gastroenterology,2008.22(2):329-340.
[43] Chan Fion S. and Chu Kent-Man, Capsule Endoscopy for Gastrointestinal Bleeding of Obscure Origin,Asian Journal of Surgery,2008.31(2):96-99.
[44] Daas Adel Y., Small Michael B., Pinkas Haim, et al., Safety of conventional and wireless capsuleendoscopy in patients supported with nonpulsatile axial flow Heart-Mate II left ventricular assistdevice, Gastrointestinal Endoscopy,2008.68(2):379-382.
[45] Golding M., Doman D. and Goldberg H., Use of wireless capsule endoscopy for evaluation ofsustained gastric presence of a polymer medication delivery system, American Journal ofGastroenterology,2008.103:S41-S41.
[46] Macdonald Jonathan, Porter Victoria and McNamara Deirdre, Negative capsule endoscopy in patientswith obscure GI bleeding predicts low rebleeding rates, Gastrointestinal Endoscopy,2008.68(6):1122-1127.
[47] Muhammad A., Khara H., Bajaj P., et al., A single-center experience of831patients with wirelesscapsule endoscopy, American Journal of Gastroenterology,2008.103:S510-S510.
[48] Spada C., Riccioni M. E., Familiari P., et al., Video capsule endoscopy in small-bowel tumours: Asingle centre experience, Scandinavian Journal of Gastroenterology,2008.43(4):497-505.
[49] Yoo J. Helen, Tarbox Jonathan and Granpeesheh Doreen, Using stimulus fading to teach a young childwith autism to ingest wireless capsule endoscopy, Gastrointestinal Endoscopy,2008.67(7):1203-1204.
[50] Yun Sub Jung, Young Ho Kim, Dong Ha Lee, et al. Active Blood Detection in a High ResolutionCapsule Endoscopy using Color Spectrum Transformation.2008International Conference onBioMedical Engineering and Informatics.2008. Sanya, Hainan, China IEEE computersociety,Washington, DC, USA859-862.
[51] Buscaglia Jonathan M., Giday Samuel A., Kantsevoy Sergey V., et al., Performance Characteristics ofthe Suspected Blood Indicator Feature in Capsule Endoscopy According to Indication for Study,Clinical Gastroenterology and Hepatology,2008.6(3):298-301.
[52] Liangpunsakul Suthat, Mays Lori and Rex Douglas K., Performance of given suspected bloodindicator, The American Journal of Gastroenterology,2003.98(12):2676-2678.
[53] Mackiewicz M., Fisher M. and Jamieson C. Bleeding detection in Wireless Capsule Endoscopy usingadaptive colour histogram model and Support Vector Classification.Medical Imaging2008Conference.2008. San Diego, CA: Spie-Int Soc Optical Engineering:R1-R12.
[54] Bourbakis N. Detecting abnormal patterns in WCE images. Bioinformatics and Bioengineering,2005. BIBE2005. Fifth IEEE Symposium on.2005:232-238.
[55] Baopu Li and Meng M. Q. H. Analysis of wireless capsule endoscopy images using chromaticitymoments.Robotics and Biomimetics,2007. ROBIO2007. IEEE International Conference on.2007:87-92.
[56] Baopu Li and Meng Q. H. Wireless Capsule Endoscopy Images Enhancement using Contrast DrivenForward and Backward Anisotropic Diffusion.IEEE International Conference on Image Processing,2007.2007:437-440.
[57] Hwang Sae, Oh JungHwan, Cox Jay, et al. Blood detection in wireless capsule endoscopy usingexpectation maximization clustering.Image Processing Medical Imaging2006:.2006. San Diego, CA,USA: SPIE:1-11.
[58] Phooi Yee Lau and Correia P. L. Detection of bleeding patterns in WCE video using multiplefeatures.29th Annual International Conference of the IEEE, Engineering in Medicine and BiologySociety,2007.2007:5601-5604.
[59] Li B. P. and Meng Q. H., Computer aided detection of bleeding in capsule endoscopy images, in2008Conference on Electrical and Computer Engineering.2008: Niagara Falls, CANADA:1875-1878.
[60] Mylonaki M., Fritscher-Ravens A. and Swain P., Wireless capsule endoscopy: a comparison with pushenteroscopy in patients with gastroscopy and colonoscopy negative gastrointestinal bleeding, Gut,2003.52(8):1122-1126.
[61]严振国,正常人体解剖学,上海科学技术出版社,1996:98-105.
[62]楚宪襄and李名扬等,人体解剖学,河南医科大学出版社,1997:93-110.
[63]成令忠,王一飞and钟翠平,组织胚胎学,上海科学技术文献出版社,2003
[64]张经济，连至诚，许冠荪，等,消化道生理及病理生理学：基础与临床,1997:1.
[65]周吕and柯美云,胃肠动力学, in科学出版社.1999:北京:112-114.
[66]许国铭,胃肠动力研究进展,云南医药,1997.18(4):312-315.
[67] JH Meyer, J Dressman, AS Fink, et al., Effect of size and density on gastric emptying indigestiblesolids, Gastroenterology,1985.89:805-813.
[68]张炜,李洁,陈英俊, et al.,被动式胶囊内窥镜在小肠内运动的力学分析,工程设计学报,2006.13(5):350-354.
[69] Gabriel C, Gabriel S and Corthout E, The dielectric properties of biological tissues: I. Literaturesurvey Physics in Medicine and Biology,1996.41:2231-2249
[70] Lenaerts Bert and Puers Robert, An inductive power link for a wireless endoscope, Biosensors andBioelectronics,2007.22(7):1390-1395.
[71]林良明,机电系统在医疗工程中的应用研究,中国医疗器械信息,2003.9(5):4-7.
[72]章毓晋,图像工程-图像处理.第二版ed.2007,北京:清华大学出版社:164-173.
[73] Gonzalez Rafael C. and Woods Richard E., Digital Image Procession Second Edition.2rd ed.2006,Beijing: Publishing House of Electronics Industry:7.
[74]付贤松, CMOS图像传感器动态范围扩展技术的研究.2006,天津大学:天津
[75]程开富,新颖CCD图像传感器最新发展及应用,集成电路通讯,2006(24):30-38.
[76]黄美玲,张伯珩,边川平, et al., CCD和CMOS图像传感器性能比较,科学技术与工程,2007(7):249-251.
[77]蔡文贵, CCD技术及应用.1992,北京:电了工业出版社:123-137.
[78] Technology Micron, CMOS Digital Image Sensor MT9D131datasheet.2008: Missouri, USA
[79] INSTRUMENTS TEXAS, smartRF CC1100data sheet.2005: Texas, USA
[80] Couch Leon W., Digital and Analog Communication Systems,数字与模拟通信系统.6th ed.国外电子通信教材.2002,北京:电子工业出版社
[81] Maxim Integrated Products Inc., MAX1725/1726datasheet.2003: Sunnyvale, CA
[82]余兆明and余智,数字电视原理.2009,西安:西安电子科技大学出版社:5-16.
[83] ROBIN Michael and POULIN Michel,数字电视基础.第二版ed.2003,北京:人民邮电出版社:34-63.
[84] Omnivision Technologies Inc.(2006,September27) OV6920CMOS Analog NTSC Camerchip Sensorwith OmniPixel Technology.1,2-7.
[85] Omnivision Technologies Inc.(2002) Serial Camera Control Bus Functional Specification.2.
[86] Thumim A, Reed G, Lupo F, et al., High power electromagnetic energy transfer for totally implanteddevices, IEEE Transactions on Magnetics,1970.6(2):326-332.
[87] C. Schuder J., H. Gold J. and Jr Stephens He., Ultrahigh power electromagnetic energy transport intothe body, Transactions American Society for Artificial Internal Organs,1971.17:406-415
[88] C. Schuder J., H. Gold J. and Jr Stephens He., An inductively coupled RF system for the transmissionof1kW of power through the skin, IEEE Transactions on Biomedical Engineering,1971.18(4):265-273.
[89] Matsuki H., Shiiki M., Murakami K., et al., investigation of coil geometry for transcutaneous energytransmission for artificial heart, Ieee Transactions on Magnetics,1992.28(5):2406-2408.
[90] Yamane T., The present and future state of nonpulsatile artificial heart technology, Journal of ArtificialOrgans,2002.5(3):0149-0155.
[91] Sato F., Nomoto T., Kano G., et al., A new contactless power-signal transmission device for implantedfunctional electrical stimulation (FES), Magnetics, IEEE Transactions on,2004.40(4):2964-2966.
[92] Wang G. X., Liu W. T., Bashirullah R., et al., A closed loop transcutaneous power transfer system forImplantable devices with enhanced stability,2004Ieee International Symposium on Circuits andSystems, Vol4, Proceedings,2004:17-20.
[93] Wang G. X., Liu W. T., Sivaprakasam M., et al., Design and analysis of an adaptive transcutaneouspower telemetry for biomedical implants, Ieee Transactions on Circuits and Systems I-Regular Papers,2005.52(10):2109-2117.
[94] Tsai C. C., Chen B. S. and Tsai C. M., Design of wireless transcutaneous energy transmission systemfor totally artificial hearts,2000Ieee Asia-Pacific Conference on Circuits and Systems,2000:646-649.
[95]赵春宇,陈大跃and高立明,植入式电动智能掌指关节的研究,高技术通讯,1999.12:28-31.
[96] Matsuki H., Shiiki M., Murakami K., et al., Investigation of coil geometry for transcutaneous energytransmission for artificial heart, Magnetics, IEEE Transactions on,1992.28(5):2406-2408.
[97]赵凯华and陈熙谋,电磁学.2003,北京:高等教育出版社
[98] Ryu Munho, Kim Jong Dae, Chin Hyun Uk, et al., Three-dimensional power receiver for in vivorobotic capsules, Medical and Biological Engineering and Computing,2007.45(10):997-1002.
[99]孟章荣,各种颜色模型选用需求分析,中国图像图形学报,1996.1(3):238-241.
[100] Takagi Mikio and Shimoda Haruhisa,图像处理技术手册.2007,北京: Science Press:416-423.
[101] Takagi Mikio and Shimoda Haruhisa, Handbook of Image analysis.2007, Beijing: SciencePress:428-440.
[102]吕新广,包装色彩学.2001,郑州:印刷工业出版社
[103]杭州商学院and北京财贸学院,统计学原理.1981,北京:经济科学出版社:109-112.
[104]章毓晋,图像工程-图像分析.第二版ed.2007,北京:清华大学出版社:102-103.
[105]龚声蓉,刘纯平and王强,数字图像处理与分析.2006,北京:清华大学出版社:190-193.
[106]姚敏,数字图像处理.2006,北京:机械工业出版社:250-251.
[107] DG Altman and JM Bland, Diagnostic tests.1: Sensitivity and specificity., BMJ,1994.308(6943):1552.
[108] PAN Guobing, YAN Guozheng, SONG XinShuai, et al., BP neural network classification for bleedingdetection in wireless capsule endoscopy, Journal of Medical Engineering&Technology,2009.33(7):575-581.
[109]冯定,神经网络专家系统.2006,北京:科学出版社:1-8.
[110]史忠植,神经网络.2009,北京:高等教育出版社:1-7.
[111]阮晓钢,神经计算科学. Vol.1.2006,北京:国防工业出版社:2-5.
[112] Hagan Martin T., Demuth Howard B. and Beale Mark H.,神经网络设计(Neural Network Design).2002,北京:机械工业出版社:5-6.
[113]韩力群,人工神经网络教程.2006,北京:北京邮电大学出版社:14-19.
[114] Rosenblatt F., a probabilistic model for information storage and organization in the brain,Psychological Review,1958.65(6):386-408.
[115] E. Bryson A. and C. Ho Y., applied optimal control.1969, New York: Blaisdell
[116] Rumelhart David E., Hinton Geoffrey E. and Williams Ronald J., Learning representations byback-propagating errors, Nature,1986.323(6088):533-536.
[117] Specht D. F., Probabilistic neural networks for classification, mapping, or associative memory, IEEEInternational Conference on Neural Networks,1988.1:525-532.
[118] Specht D. F., PROBABILISTIC NEURAL NETWORKS, Neural Networks,1990.3(1):109-118.
[119] Specht D. F., APPLICATIONS OF PROBABILISTIC NEURAL NETWORKS, Applications ofArtificial Neural Networks,1990.1294:344-353.
[120] Specht D. F. and Shapiro P. D., TRAINING SPEED COMPARISON OF PROBABILISTIC NEURALNETWORKS WITH BACK-PROPAGATION NETWORKS, International Neural NetworkConference, Vols1and2,1990:440-443.
[121]葛哲学and孙志强,神经网络理论与MATLAB实现.2008,北京:电子工业出版社:117-126.
[122]史忠植,神经网络.2009,北京:高等教育出版社:203-211.
[123]张青贵,人工神经网络导论.2004,北京:中国水利水电出版社:107-111.
[124] Mathworks The. Probabilistic Neural Networks.2009[cited20095.21]; Available from:http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/index.html.
[125] D. Spech T., Probabilistic neural network, Neural Networks,1990.3(1):109～118.
[126]洪喜,续志军and杨宁,基于径向基函数网络的光电编码器误差补偿法,光学精密工程,2008.16(4):598-604.
[127]张志伟,孔凡让,赵吉文, et al.,对弈机器人的视觉图像处理和识别,计算机应用与软件,2008.125(2):215-217.
[128]陈菲,基于BP神经网络的超声肝图像识别,兵工自动化网络与信息技术,2007.26(8):43-44.
[129]张海波,董槐林,飞龙, et al.,基于BP神经网络的图像识别研究,计算机与现代化,2008.153(5):17-19.
[130] H HollandJ, AdaptationinNaturalandArtifieialSystem.2nd ed.1992, Michigan: MIT Press
[131]席裕庚,柴天佑and恽为民,遗传算法综述,控制理论与应用,1996.13(6):697-708.
[132] Storn Rainer and Price Kenneth, Differential Evolution-A simple and effieient adaptive seheme forglobal optimization over continuous spaces, in Technical report TR-95-012.1995: Berkeley
[133] Storn R. and Price K., Minimizing the real functions of the ICEC'96contest by differential evolution,1996Ieee International Conference on Evolutionary Computation (Icec '96), Proceedings Of,1996:842-844.
[134] Storn R. and Price K., Differential evolution-A simple and efficient heuristic for global optimizationover continuous spaces, Journal of Global Optimization,1997.11(4):341-359.
[135] Lampinen J. and Storn R., Differential evolution, New Optimization Techniques in Engineering,2004.141:123-166.
[136] Storn R., Designing nonstandard filters with differential evolution, Ieee Signal Processing Magazine,2005.22(1):103-106.
[137] Storn R., On the usage of differential evolution for function optimization,1996Biennial Conference ofthe North American Fuzzy Information Processing Society-Nafips,1996:519-523.
[138] Storn R., Differential evolution design of an IIR-filter,1996Ieee International Conference onEvolutionary Computation (Icec '96), Proceedings Of,1996:268-273.
[139]武志峰,差异演化算法及其应用研究, in计算机与信息技术学院.2009,北京交通大学:北京
[140] Mezura-Montes E., Velazquez-Reyes J. and Coello C. A. C., A comparative study of differentialevolution variants for global optimization, GECCO2006: Genetic and Evolutionary ComputationConference, Vol1and2,2006:485-492.
[141] Goudos S. K., Zaharis Z. D., Baltzis K. B., et al., A Comparative Study of Particle SwarmOptimization and Differential Evolution on Radar Absorbing Materials Design for EMC Applications,Emc Europe:2009International Symposium on Electromagnetic Compatibility,2009:45-48.
[142] Vesterstrom J. and Thomsen R., A comparative study of differential evolution, particle swarmoptimization, and evolutionary algorithms on numerical benchmark problems, Cec2004: Proceedingsof the2004Congress on Evolutionary Computation, Vols1and2,2004:1980-1987.
[2] Mc Caffrey C., Chevalerias O., O'Mathuna C., et al., Swallowable-capsule technology, IEEEPervasive Computing,2008.7(1):23-29.
[3] Clearinghouse National digestive diseases information, Bleeding in the Digestive Tract. Vol.07.2004,Bethesda, Maryland: National Institutes of Health
[4] Lewis Blair S., SMALL INTESTINAL BLEEDING, Gastroenterology Clinics of North America,2000.29(1):67-95.
[5] Zuckerman Gary R., Prakash Chandra, Askin Matthew P., et al., AGA technical review on theevaluation and management of occult and obscure gastrointestinal bleeding, Gastroenterology,2000.118(1):201-221.
[6] M. HOWARTH D., K. TANG and W. LEES, The clinical utility of nuclear medicine imaging for thedetection of occult gastrointestinal haemorrhage, Nucl Med Commun,2002.23(6):591-594.
[7] Zworykin V.K., A`Radio Pill', Nature,1957.179(4566):898.
[8] MACKAY R. STUART and JACOBSON BERTIL, Endoradiosonde, Nature,1957.179(4572):1239-1240.
[9] J. Fallingborg, Intraluminal pH of the human gastrointestinal tract, Danish medical bulletin,1999.46(3):183-96.
[10] Eliakim R., Suissa A., Yassin K., et al., Wireless capsule video endoscopy compared to bariumfollow-through and computerised tomography in patients with suspected Crohn's disease--final report,Digestive and Liver Disease,2004.36(8):519-522.
[11]郑小林,无线胶囊式小肠内窥镜技术进展,中国医疗器械信息,2004.10(4):6-8.
[12] Bourbakis N., Makrogiannis S. and Kavraki D. A neural network-based detection of bleeding insequences of WCE images.5th IEEE Symposium on Bioinformatics and Bioengineering.2005.Minneapolis, MN: Ieee Computer Soc:324-327.
[13] Iddan Gavriel, Meron Gavriel, Glukhovsky Arkady, et al., Wireless capsule endoscopy, Nature,2000.405(6785):417-417.
[14] Swain P., Iddan G., Meron G., et al., Wireless capsule endoscopy of the small-bowel. Development,testing and first human trials, Biomonitoring and Endoscopy Technologies,2001.1(29):19-23.
[15] Meron G. D., The development of the swallowable video capsule (M2A), Gastrointestinal Endoscopy,2000.52(6):817-819.
[16] Glukhovsky A., Wireless capsule endoscopy, Sensor Review,2003.23(2):128-33.
[17] Co. Given Imaging. PillCam ESO2.2007[cited2007June14]; Available from:http://www.medgadget.com/archives/2007/06/pillcam_eso_2.html.
[18] Co. Given Imaging. The Third Generation PillCam SB Launched.2005[cited2005October13];Available from: http://medgadget.com/archives/2005/10/the_third_gener.html.
[19] Co. Olympus Optical, Medical Camera System, Office P.a.T., Editor.1981: USA
[20] Co. Olympus, Olympus Launches High-resolution Capsule Endoscope in Europe.2008
[21] Co. Olympus Optical, Capsule Endoscope, office P.a.T., Editor.2007: USA
[22] Lab RF. System. The Next Generation Capsule Endoscope.2008March,15,2008[cited200811st,Oct.]; Available from: http://www.rfamerica.com/sayaka/index.html.
[23] Park H.J., Nam H.W., Song B.S., et al. Design of bi-directional and multi-channel miniaturizedtelemetrymodule for wireless endoscopy.2nd Annual International IEEE-EMB Special TopicConference on Microtechnologies in Medicine&Biology.2002. Madison, WI, USA:273-276.
[24] S Bang, J.Y. Park, S. Jeong, et al., First clinical trial of the "MiRo" capsule endoscope by using anovel transmission technology: electric-field propagation, Gastrointestinal Endoscopy,2009.69(2):253-259.
[25]吴复刚,张智高and贾爱芹, OMOM胶囊内镜检查疑诊小肠出血37例分析,中国实用医药,2009.9:75-76.
[26]金安琴,黄晓俊and刘子燕, OMOM胶囊内镜对小肠疾病的诊断价值初步研究,临床消化病杂志,2009.21(1):28-30.
[27] LI Chen-yi, ZHANG Bing-ling, CHEN Chun-xiao, et al., OMOM capsule endoscopy in diagnosis ofsmall bowel disease, Journal of Zhejiang University(Science B),2008.9(11):857-862.
[28]马莹,马彩虹and谭玉娥,国产OMOM胶囊内镜临床应用分析,延安大学学报(医学科学版),2008.24(5):388-389.
[29]谢翔,李国林,张春, et al.,一种双向、数字式微型无线内窥镜系统设计,固体电子学研究与进展,2007(27):123-129.
[30] Zhihua Wang, Xiang Xie, Chun Zhang, et al. IC design for a digital wireless endoscope capsulesystem.Radio Science Conference.2004:K42-K43.
[31]谢翔,李国林,池保勇, et al.,双向、数字化的微型无线内窥镜胶囊内数模混合电路设计,清华大学学报（自然科学版）,2005.45(10):1368-1372.
[32]陈新凯,谢翔,秦豫, et al.,无线内窥镜专用芯片的FPGA验证及相关测试,半导体技术,2005.30(9):46-49.
[33] Chi Baoyong, Yao Jinke, Han Shuguang, et al., Low power high data rate wireless endoscopytransceiver, Microelectronics Journal.38(10-11):1070-1081.
[34]简小云,梅涛and汪小华,胶囊内窥镜机器人的外磁场驱动方法,机器人,2005.27(4):367-372.
[35]付国强,梅涛and孔德义,无线微型机器人肠道内窥镜系统中图像采集与无线传输子系统的设计,光学精密工程,2002.10(6):614-618.
[36]王辉静and简小云,体内医疗微型机器人的外磁场驱动与控制系统研究,深圳信息职业技术学院学报,2006.4(4):10-13.
[37] Appleyard M., Glukhovsky A. and Swain P., Wireless-capsule diagnostic endoscopy for recurrentsmall-bowel bleeding, New England Journal of Medicine,2001.344(3):232-233.
[38] Storch Ian M., Schmelkin Ira, Vance Shirley, et al., A Double Blind Placebo Controlled Study ofTegaserod to Increase Gastric and Small Bowel Transit Times with Wireless Capsule Endoscocpy(WCE): Preliminary Data, Gastrointestinal Endoscopy,2006.63(5):AB192-AB192.
[39] Sturniolo Giacomo C., Di Leo Vincenza, Vettorato Maria G., et al., Small Bowel Exploration byWireless Capsule Endoscopy: Results from314Procedures, American Journal of Medicine,2006.119(4):341-347.
[40] Canlas Karen R, Dobozi Brian M, Lin Sauyu, et al., Using capsule endoscopy to identify GI tractlesions in cirrhotic patients with portal hypertension and chronic anemia, Journal of ClinicalGastroenterology,2008.42(7):844-848.
[41] Carretero C., Fernandez-Urien I., Betes M., et al., Role of videocapsule endoscopy for gastrointestinalbleeding, World Journal of Gastroenterology,2008.14(34):5261-5264.
[42] Cellier C., Obscure gastrointestinal bleeding: Role of videocapsule and double-balloon enteroscopy,Best Practice&Research in Clinical Gastroenterology,2008.22(2):329-340.
[43] Chan Fion S. and Chu Kent-Man, Capsule Endoscopy for Gastrointestinal Bleeding of Obscure Origin,Asian Journal of Surgery,2008.31(2):96-99.
[44] Daas Adel Y., Small Michael B., Pinkas Haim, et al., Safety of conventional and wireless capsuleendoscopy in patients supported with nonpulsatile axial flow Heart-Mate II left ventricular assistdevice, Gastrointestinal Endoscopy,2008.68(2):379-382.
[45] Golding M., Doman D. and Goldberg H., Use of wireless capsule endoscopy for evaluation ofsustained gastric presence of a polymer medication delivery system, American Journal ofGastroenterology,2008.103:S41-S41.
[46] Macdonald Jonathan, Porter Victoria and McNamara Deirdre, Negative capsule endoscopy in patientswith obscure GI bleeding predicts low rebleeding rates, Gastrointestinal Endoscopy,2008.68(6):1122-1127.
[47] Muhammad A., Khara H., Bajaj P., et al., A single-center experience of831patients with wirelesscapsule endoscopy, American Journal of Gastroenterology,2008.103:S510-S510.
[48] Spada C., Riccioni M. E., Familiari P., et al., Video capsule endoscopy in small-bowel tumours: Asingle centre experience, Scandinavian Journal of Gastroenterology,2008.43(4):497-505.
[49] Yoo J. Helen, Tarbox Jonathan and Granpeesheh Doreen, Using stimulus fading to teach a young childwith autism to ingest wireless capsule endoscopy, Gastrointestinal Endoscopy,2008.67(7):1203-1204.
[50] Yun Sub Jung, Young Ho Kim, Dong Ha Lee, et al. Active Blood Detection in a High ResolutionCapsule Endoscopy using Color Spectrum Transformation.2008International Conference onBioMedical Engineering and Informatics.2008. Sanya, Hainan, China IEEE computersociety,Washington, DC, USA859-862.
[51] Buscaglia Jonathan M., Giday Samuel A., Kantsevoy Sergey V., et al., Performance Characteristics ofthe Suspected Blood Indicator Feature in Capsule Endoscopy According to Indication for Study,Clinical Gastroenterology and Hepatology,2008.6(3):298-301.
[52] Liangpunsakul Suthat, Mays Lori and Rex Douglas K., Performance of given suspected bloodindicator, The American Journal of Gastroenterology,2003.98(12):2676-2678.
[53] Mackiewicz M., Fisher M. and Jamieson C. Bleeding detection in Wireless Capsule Endoscopy usingadaptive colour histogram model and Support Vector Classification.Medical Imaging2008Conference.2008. San Diego, CA: Spie-Int Soc Optical Engineering:R1-R12.
[54] Bourbakis N. Detecting abnormal patterns in WCE images. Bioinformatics and Bioengineering,2005. BIBE2005. Fifth IEEE Symposium on.2005:232-238.
[55] Baopu Li and Meng M. Q. H. Analysis of wireless capsule endoscopy images using chromaticitymoments.Robotics and Biomimetics,2007. ROBIO2007. IEEE International Conference on.2007:87-92.
[56] Baopu Li and Meng Q. H. Wireless Capsule Endoscopy Images Enhancement using Contrast DrivenForward and Backward Anisotropic Diffusion.IEEE International Conference on Image Processing,2007.2007:437-440.
[57] Hwang Sae, Oh JungHwan, Cox Jay, et al. Blood detection in wireless capsule endoscopy usingexpectation maximization clustering.Image Processing Medical Imaging2006:.2006. San Diego, CA,USA: SPIE:1-11.
[58] Phooi Yee Lau and Correia P. L. Detection of bleeding patterns in WCE video using multiplefeatures.29th Annual International Conference of the IEEE, Engineering in Medicine and BiologySociety,2007.2007:5601-5604.
[59] Li B. P. and Meng Q. H., Computer aided detection of bleeding in capsule endoscopy images, in2008Conference on Electrical and Computer Engineering.2008: Niagara Falls, CANADA:1875-1878.
[60] Mylonaki M., Fritscher-Ravens A. and Swain P., Wireless capsule endoscopy: a comparison with pushenteroscopy in patients with gastroscopy and colonoscopy negative gastrointestinal bleeding, Gut,2003.52(8):1122-1126.
[61]严振国,正常人体解剖学,上海科学技术出版社,1996:98-105.
[62]楚宪襄and李名扬等,人体解剖学,河南医科大学出版社,1997:93-110.
[63]成令忠,王一飞and钟翠平,组织胚胎学,上海科学技术文献出版社,2003
[64]张经济，连至诚，许冠荪，等,消化道生理及病理生理学：基础与临床,1997:1.
[65]周吕and柯美云,胃肠动力学, in科学出版社.1999:北京:112-114.
[66]许国铭,胃肠动力研究进展,云南医药,1997.18(4):312-315.
[67] JH Meyer, J Dressman, AS Fink, et al., Effect of size and density on gastric emptying indigestiblesolids, Gastroenterology,1985.89:805-813.
[68]张炜,李洁,陈英俊, et al.,被动式胶囊内窥镜在小肠内运动的力学分析,工程设计学报,2006.13(5):350-354.
[69] Gabriel C, Gabriel S and Corthout E, The dielectric properties of biological tissues: I. Literaturesurvey Physics in Medicine and Biology,1996.41:2231-2249
[70] Lenaerts Bert and Puers Robert, An inductive power link for a wireless endoscope, Biosensors andBioelectronics,2007.22(7):1390-1395.
[71]林良明,机电系统在医疗工程中的应用研究,中国医疗器械信息,2003.9(5):4-7.
[72]章毓晋,图像工程-图像处理.第二版ed.2007,北京:清华大学出版社:164-173.
[73] Gonzalez Rafael C. and Woods Richard E., Digital Image Procession Second Edition.2rd ed.2006,Beijing: Publishing House of Electronics Industry:7.
[74]付贤松, CMOS图像传感器动态范围扩展技术的研究.2006,天津大学:天津
[75]程开富,新颖CCD图像传感器最新发展及应用,集成电路通讯,2006(24):30-38.
[76]黄美玲,张伯珩,边川平, et al., CCD和CMOS图像传感器性能比较,科学技术与工程,2007(7):249-251.
[77]蔡文贵, CCD技术及应用.1992,北京:电了工业出版社:123-137.
[78] Technology Micron, CMOS Digital Image Sensor MT9D131datasheet.2008: Missouri, USA
[79] INSTRUMENTS TEXAS, smartRF CC1100data sheet.2005: Texas, USA
[80] Couch Leon W., Digital and Analog Communication Systems,数字与模拟通信系统.6th ed.国外电子通信教材.2002,北京:电子工业出版社
[81] Maxim Integrated Products Inc., MAX1725/1726datasheet.2003: Sunnyvale, CA
[82]余兆明and余智,数字电视原理.2009,西安:西安电子科技大学出版社:5-16.
[83] ROBIN Michael and POULIN Michel,数字电视基础.第二版ed.2003,北京:人民邮电出版社:34-63.
[84] Omnivision Technologies Inc.(2006,September27) OV6920CMOS Analog NTSC Camerchip Sensorwith OmniPixel Technology.1,2-7.
[85] Omnivision Technologies Inc.(2002) Serial Camera Control Bus Functional Specification.2.
[86] Thumim A, Reed G, Lupo F, et al., High power electromagnetic energy transfer for totally implanteddevices, IEEE Transactions on Magnetics,1970.6(2):326-332.
[87] C. Schuder J., H. Gold J. and Jr Stephens He., Ultrahigh power electromagnetic energy transport intothe body, Transactions American Society for Artificial Internal Organs,1971.17:406-415
[88] C. Schuder J., H. Gold J. and Jr Stephens He., An inductively coupled RF system for the transmissionof1kW of power through the skin, IEEE Transactions on Biomedical Engineering,1971.18(4):265-273.
[89] Matsuki H., Shiiki M., Murakami K., et al., investigation of coil geometry for transcutaneous energytransmission for artificial heart, Ieee Transactions on Magnetics,1992.28(5):2406-2408.
[90] Yamane T., The present and future state of nonpulsatile artificial heart technology, Journal of ArtificialOrgans,2002.5(3):0149-0155.
[91] Sato F., Nomoto T., Kano G., et al., A new contactless power-signal transmission device for implantedfunctional electrical stimulation (FES), Magnetics, IEEE Transactions on,2004.40(4):2964-2966.
[92] Wang G. X., Liu W. T., Bashirullah R., et al., A closed loop transcutaneous power transfer system forImplantable devices with enhanced stability,2004Ieee International Symposium on Circuits andSystems, Vol4, Proceedings,2004:17-20.
[93] Wang G. X., Liu W. T., Sivaprakasam M., et al., Design and analysis of an adaptive transcutaneouspower telemetry for biomedical implants, Ieee Transactions on Circuits and Systems I-Regular Papers,2005.52(10):2109-2117.
[94] Tsai C. C., Chen B. S. and Tsai C. M., Design of wireless transcutaneous energy transmission systemfor totally artificial hearts,2000Ieee Asia-Pacific Conference on Circuits and Systems,2000:646-649.
[95]赵春宇,陈大跃and高立明,植入式电动智能掌指关节的研究,高技术通讯,1999.12:28-31.
[96] Matsuki H., Shiiki M., Murakami K., et al., Investigation of coil geometry for transcutaneous energytransmission for artificial heart, Magnetics, IEEE Transactions on,1992.28(5):2406-2408.
[97]赵凯华and陈熙谋,电磁学.2003,北京:高等教育出版社
[98] Ryu Munho, Kim Jong Dae, Chin Hyun Uk, et al., Three-dimensional power receiver for in vivorobotic capsules, Medical and Biological Engineering and Computing,2007.45(10):997-1002.
[99]孟章荣,各种颜色模型选用需求分析,中国图像图形学报,1996.1(3):238-241.
[100] Takagi Mikio and Shimoda Haruhisa,图像处理技术手册.2007,北京: Science Press:416-423.
[101] Takagi Mikio and Shimoda Haruhisa, Handbook of Image analysis.2007, Beijing: SciencePress:428-440.
[102]吕新广,包装色彩学.2001,郑州:印刷工业出版社
[103]杭州商学院and北京财贸学院,统计学原理.1981,北京:经济科学出版社:109-112.
[104]章毓晋,图像工程-图像分析.第二版ed.2007,北京:清华大学出版社:102-103.
[105]龚声蓉,刘纯平and王强,数字图像处理与分析.2006,北京:清华大学出版社:190-193.
[106]姚敏,数字图像处理.2006,北京:机械工业出版社:250-251.
[107] DG Altman and JM Bland, Diagnostic tests.1: Sensitivity and specificity., BMJ,1994.308(6943):1552.
[108] PAN Guobing, YAN Guozheng, SONG XinShuai, et al., BP neural network classification for bleedingdetection in wireless capsule endoscopy, Journal of Medical Engineering&Technology,2009.33(7):575-581.
[109]冯定,神经网络专家系统.2006,北京:科学出版社:1-8.
[110]史忠植,神经网络.2009,北京:高等教育出版社:1-7.
[111]阮晓钢,神经计算科学. Vol.1.2006,北京:国防工业出版社:2-5.
[112] Hagan Martin T., Demuth Howard B. and Beale Mark H.,神经网络设计(Neural Network Design).2002,北京:机械工业出版社:5-6.
[113]韩力群,人工神经网络教程.2006,北京:北京邮电大学出版社:14-19.
[114] Rosenblatt F., a probabilistic model for information storage and organization in the brain,Psychological Review,1958.65(6):386-408.
[115] E. Bryson A. and C. Ho Y., applied optimal control.1969, New York: Blaisdell
[116] Rumelhart David E., Hinton Geoffrey E. and Williams Ronald J., Learning representations byback-propagating errors, Nature,1986.323(6088):533-536.
[117] Specht D. F., Probabilistic neural networks for classification, mapping, or associative memory, IEEEInternational Conference on Neural Networks,1988.1:525-532.
[118] Specht D. F., PROBABILISTIC NEURAL NETWORKS, Neural Networks,1990.3(1):109-118.
[119] Specht D. F., APPLICATIONS OF PROBABILISTIC NEURAL NETWORKS, Applications ofArtificial Neural Networks,1990.1294:344-353.
[120] Specht D. F. and Shapiro P. D., TRAINING SPEED COMPARISON OF PROBABILISTIC NEURALNETWORKS WITH BACK-PROPAGATION NETWORKS, International Neural NetworkConference, Vols1and2,1990:440-443.
[121]葛哲学and孙志强,神经网络理论与MATLAB实现.2008,北京:电子工业出版社:117-126.
[122]史忠植,神经网络.2009,北京:高等教育出版社:203-211.
[123]张青贵,人工神经网络导论.2004,北京:中国水利水电出版社:107-111.
[124] Mathworks The. Probabilistic Neural Networks.2009[cited20095.21]; Available from:http://www.mathworks.com/access/helpdesk/help/toolbox/nnet/index.html.
[125] D. Spech T., Probabilistic neural network, Neural Networks,1990.3(1):109～118.
[126]洪喜,续志军and杨宁,基于径向基函数网络的光电编码器误差补偿法,光学精密工程,2008.16(4):598-604.
[127]张志伟,孔凡让,赵吉文, et al.,对弈机器人的视觉图像处理和识别,计算机应用与软件,2008.125(2):215-217.
[128]陈菲,基于BP神经网络的超声肝图像识别,兵工自动化网络与信息技术,2007.26(8):43-44.
[129]张海波,董槐林,飞龙, et al.,基于BP神经网络的图像识别研究,计算机与现代化,2008.153(5):17-19.
[130] H HollandJ, AdaptationinNaturalandArtifieialSystem.2nd ed.1992, Michigan: MIT Press
[131]席裕庚,柴天佑and恽为民,遗传算法综述,控制理论与应用,1996.13(6):697-708.
[132] Storn Rainer and Price Kenneth, Differential Evolution-A simple and effieient adaptive seheme forglobal optimization over continuous spaces, in Technical report TR-95-012.1995: Berkeley
[133] Storn R. and Price K., Minimizing the real functions of the ICEC'96contest by differential evolution,1996Ieee International Conference on Evolutionary Computation (Icec '96), Proceedings Of,1996:842-844.
[134] Storn R. and Price K., Differential evolution-A simple and efficient heuristic for global optimizationover continuous spaces, Journal of Global Optimization,1997.11(4):341-359.
[135] Lampinen J. and Storn R., Differential evolution, New Optimization Techniques in Engineering,2004.141:123-166.
[136] Storn R., Designing nonstandard filters with differential evolution, Ieee Signal Processing Magazine,2005.22(1):103-106.
[137] Storn R., On the usage of differential evolution for function optimization,1996Biennial Conference ofthe North American Fuzzy Information Processing Society-Nafips,1996:519-523.
[138] Storn R., Differential evolution design of an IIR-filter,1996Ieee International Conference onEvolutionary Computation (Icec '96), Proceedings Of,1996:268-273.
[139]武志峰,差异演化算法及其应用研究, in计算机与信息技术学院.2009,北京交通大学:北京
[140] Mezura-Montes E., Velazquez-Reyes J. and Coello C. A. C., A comparative study of differentialevolution variants for global optimization, GECCO2006: Genetic and Evolutionary ComputationConference, Vol1and2,2006:485-492.
[141] Goudos S. K., Zaharis Z. D., Baltzis K. B., et al., A Comparative Study of Particle SwarmOptimization and Differential Evolution on Radar Absorbing Materials Design for EMC Applications,Emc Europe:2009International Symposium on Electromagnetic Compatibility,2009:45-48.
[142] Vesterstrom J. and Thomsen R., A comparative study of differential evolution, particle swarmoptimization, and evolutionary algorithms on numerical benchmark problems, Cec2004: Proceedingsof the2004Congress on Evolutionary Computation, Vols1and2,2004:1980-1987.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |