基于超声图像数据挖掘的HIFU无损监控关键技术研究
|
摘要
超声引导的HIFU治疗系统以其无损,无辐射,价格相对低廉等独特的优点,在肿瘤临床治疗中有着广阔的发展前景。作为HIFU相关技术中的热点问题,对无损监控及检测治疗效果的研究,虽然引起广泛关注,并取得了一定的研究成果,但仍未见成熟的、可应用于临床的方法问世。
目前基于超声信号及超声图像的无损测温、测组织损伤程度方法,多建立在理想化的声场或温场模型基础上,并要求预知各种组织的声特性,给方法的实施带来很大困难。而从大量的、不完全的、有噪声的、模糊的实际应用数据中,提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的数据挖掘技术,在医学数据分析、内在规律找寻的研究中表现出越来越强的优越性。
对本课题的核心研究内容为:挖掘对超声图像中蕴含的可以辅助监控、检测HIFU治疗效果的信息,寻找其中的规律,最终形成易于理解和接受的、可量化的HIFU治疗效果评价。
作为一种高温热疗手段,治疗过程中靶区的温度是临床所关注的一个主要参数。论文对HIFU超声图像信息中与温度相关信息的挖掘建立在组织受热会导致超声图像纹理发生改变这一假设的基础上。采用多种纹理包括灰度共生矩阵、分形盒维数方法、小波分解等分析方法,分别提取HIFU辐照后各个温度下组织超声原始图像,及其与基准温度(37℃)下的超声图像之间的减影图像的纹理参数。结果表明:超声原始图像纹理参数与温度间不存在明显的、易于量化的函数关系;但是超声减影图像的纹理参数与温度间均表现出了较好的相关性。对其中优选参数进行与温度间的回归分析表明,HIFU焦域中心温度与超声减影图像的灰度共生矩阵参数中的四个参数与小波分解系数能量参数在40℃—80℃之间存在线性关系。采用8个与温度存在线性关系的纹理参数进行基于主成分分析的多元线性回归,40℃—70℃以下,回归方程的精度为3℃。
HIFU监控所关注的另一个参数是辐照对组织实际形成的损伤程度。一本文提出一种亚象素级基于超声图像的HIFU束损伤监控方法,通过追踪特征点的位移及变化,并针对变化程度,定义了相关距离加以量化,来实现对HIFU束损伤等级的评估。对温度与组织损伤程度、剂量与组织损伤程度、组织损伤程度与相关距离的关系进行了分析。实验结论为:温度与组织损伤等级,剂量与组织损伤等级间的关系难以量化;而辐照前后,HIFU焦域相关距离与HIFU束损伤等级之间表现出相关性,HIFU束损伤等级随相关距离的增大而增大。采用支持向量机SVM分类方法,设计分类器,分类结果表明算法可有效识别HIFU束损伤的等级。可为HIFU临床剂量的选取提供参考。
除了针对温度,组织损伤程度的监控研究外,本文还针对如何在高噪声的超声图像中,自动挖掘可以辅助诊断HIFU治疗结束后所产生的有效治疗区域做了方法上的研究。本文提出一种基于双零双速度水平集的算法,利用超声图像灰度的变化信息,完成零水平集的自动初始化,并从高噪声HIFU超声图像信息中提取HIFU有效治疗范围边缘信息及器官边缘信息。通过三维重建给出可反映HIFU有效治疗范围的大小及形状的三维信息,有望成为HIFU临床辅助治疗系统功能的有益补充。
With its advantages of non-invasive, no radiation and low cost, ultrasound guided HIFU is supposed to be a promising therapy method for tumor. As one of the key problems in HIFU related technique, ultrasound-based temperature and HIFU effect estimation have been attracting many researchers attention. But there is still no resolution which is mature enough for clinic use.
By now, most ultrasound signal or image-based non-invasive temperature or HIFU effect estimation methods are based on ideal sound field and need to know some sonic characters of tissue first. That is a very difficult condition to achieve. Data mining technology whose aim is analyzing data from different perspectives and summarizing it into useful information, has its advantages in medical data analysis and research This thesis concentrates on: Dada mining parameters from ultrasound images which can help to monitor HIFU therapy effect; Finding the rules and giving the understandable and acceptable evaluation of HIFU effect. Temperature is a very important parameter need to be monitored during HIFU therapy. Data mining temperature related ultrasound image texture parameter is based on the hypothesis that texture will change with tissue temperature. Gray Level Co-Occurrence Matrix (GLCM) , box fractal dimension, wavelet methods were used to extract texture parameters from ultrasound images at different temperature and their subtraction with ultrasound image at basic temperature(37℃). Results show that original ultrasound images' texture has no obvious relationship with temperature of tissue, but some GLCM texture parameters and wavelet coefficient energy parameters of subtraction images have linear relationship with temperature of tissue from 40℃to 80℃. Eight parameters have linear relationship with temperature were used to construct principal components analysis based multiple regression prediction model. The regression formula's precision is 3℃at temperature under 70℃.
Another way to estimate HIFU effect is monitoring lesion of tissue. A sub-pixel ultrasound image based method is proposed in this thesis and correlation distance was defined for quantization of featured points' movement and change in HIFU lesion area image. Results show that temperature has no quantitative relationship with lesion degree, neither dose, but sub-pixel cross-correlation vector field can reflect the ablation lesions position and correlation distance is helpful for detecting the degree of the beam ablation lesions. Correlation distance based SVM classification method can help to detect HIFU lesion degree automatically and efficiently.
Besides research on monitoring temperature and HIFU lesion, another theme of this thesis is to find a way which can automatically provide the HIFU lesion area's shape and position after therapy. Data mining edge information of ROI (region of interest) from high-noise ultrasound images is the key. We proposed a double zero level set, double speed approach to extract the organ and lesion area's contour. Gray level change was used to initial zero set automatically Serial "slice" contours were used to reconstruct three dimension (3D) HIFU lesion areas in the sample. Result: Experiments show that level set contour extraction method based 3D reconstruction is helpful in monitoring the size, shape and location of HIFU lesion.
目前基于超声信号及超声图像的无损测温、测组织损伤程度方法,多建立在理想化的声场或温场模型基础上,并要求预知各种组织的声特性,给方法的实施带来很大困难。而从大量的、不完全的、有噪声的、模糊的实际应用数据中,提取隐含在其中的、人们事先不知道的、但又是潜在有用的信息和知识的数据挖掘技术,在医学数据分析、内在规律找寻的研究中表现出越来越强的优越性。
对本课题的核心研究内容为:挖掘对超声图像中蕴含的可以辅助监控、检测HIFU治疗效果的信息,寻找其中的规律,最终形成易于理解和接受的、可量化的HIFU治疗效果评价。
作为一种高温热疗手段,治疗过程中靶区的温度是临床所关注的一个主要参数。论文对HIFU超声图像信息中与温度相关信息的挖掘建立在组织受热会导致超声图像纹理发生改变这一假设的基础上。采用多种纹理包括灰度共生矩阵、分形盒维数方法、小波分解等分析方法,分别提取HIFU辐照后各个温度下组织超声原始图像,及其与基准温度(37℃)下的超声图像之间的减影图像的纹理参数。结果表明:超声原始图像纹理参数与温度间不存在明显的、易于量化的函数关系;但是超声减影图像的纹理参数与温度间均表现出了较好的相关性。对其中优选参数进行与温度间的回归分析表明,HIFU焦域中心温度与超声减影图像的灰度共生矩阵参数中的四个参数与小波分解系数能量参数在40℃—80℃之间存在线性关系。采用8个与温度存在线性关系的纹理参数进行基于主成分分析的多元线性回归,40℃—70℃以下,回归方程的精度为3℃。
HIFU监控所关注的另一个参数是辐照对组织实际形成的损伤程度。一本文提出一种亚象素级基于超声图像的HIFU束损伤监控方法,通过追踪特征点的位移及变化,并针对变化程度,定义了相关距离加以量化,来实现对HIFU束损伤等级的评估。对温度与组织损伤程度、剂量与组织损伤程度、组织损伤程度与相关距离的关系进行了分析。实验结论为:温度与组织损伤等级,剂量与组织损伤等级间的关系难以量化;而辐照前后,HIFU焦域相关距离与HIFU束损伤等级之间表现出相关性,HIFU束损伤等级随相关距离的增大而增大。采用支持向量机SVM分类方法,设计分类器,分类结果表明算法可有效识别HIFU束损伤的等级。可为HIFU临床剂量的选取提供参考。
除了针对温度,组织损伤程度的监控研究外,本文还针对如何在高噪声的超声图像中,自动挖掘可以辅助诊断HIFU治疗结束后所产生的有效治疗区域做了方法上的研究。本文提出一种基于双零双速度水平集的算法,利用超声图像灰度的变化信息,完成零水平集的自动初始化,并从高噪声HIFU超声图像信息中提取HIFU有效治疗范围边缘信息及器官边缘信息。通过三维重建给出可反映HIFU有效治疗范围的大小及形状的三维信息,有望成为HIFU临床辅助治疗系统功能的有益补充。
With its advantages of non-invasive, no radiation and low cost, ultrasound guided HIFU is supposed to be a promising therapy method for tumor. As one of the key problems in HIFU related technique, ultrasound-based temperature and HIFU effect estimation have been attracting many researchers attention. But there is still no resolution which is mature enough for clinic use.
By now, most ultrasound signal or image-based non-invasive temperature or HIFU effect estimation methods are based on ideal sound field and need to know some sonic characters of tissue first. That is a very difficult condition to achieve. Data mining technology whose aim is analyzing data from different perspectives and summarizing it into useful information, has its advantages in medical data analysis and research This thesis concentrates on: Dada mining parameters from ultrasound images which can help to monitor HIFU therapy effect; Finding the rules and giving the understandable and acceptable evaluation of HIFU effect. Temperature is a very important parameter need to be monitored during HIFU therapy. Data mining temperature related ultrasound image texture parameter is based on the hypothesis that texture will change with tissue temperature. Gray Level Co-Occurrence Matrix (GLCM) , box fractal dimension, wavelet methods were used to extract texture parameters from ultrasound images at different temperature and their subtraction with ultrasound image at basic temperature(37℃). Results show that original ultrasound images' texture has no obvious relationship with temperature of tissue, but some GLCM texture parameters and wavelet coefficient energy parameters of subtraction images have linear relationship with temperature of tissue from 40℃to 80℃. Eight parameters have linear relationship with temperature were used to construct principal components analysis based multiple regression prediction model. The regression formula's precision is 3℃at temperature under 70℃.
Another way to estimate HIFU effect is monitoring lesion of tissue. A sub-pixel ultrasound image based method is proposed in this thesis and correlation distance was defined for quantization of featured points' movement and change in HIFU lesion area image. Results show that temperature has no quantitative relationship with lesion degree, neither dose, but sub-pixel cross-correlation vector field can reflect the ablation lesions position and correlation distance is helpful for detecting the degree of the beam ablation lesions. Correlation distance based SVM classification method can help to detect HIFU lesion degree automatically and efficiently.
Besides research on monitoring temperature and HIFU lesion, another theme of this thesis is to find a way which can automatically provide the HIFU lesion area's shape and position after therapy. Data mining edge information of ROI (region of interest) from high-noise ultrasound images is the key. We proposed a double zero level set, double speed approach to extract the organ and lesion area's contour. Gray level change was used to initial zero set automatically Serial "slice" contours were used to reconstruct three dimension (3D) HIFU lesion areas in the sample. Result: Experiments show that level set contour extraction method based 3D reconstruction is helpful in monitoring the size, shape and location of HIFU lesion.
引文
[1]Lynn J.A new method for the generation and use of focused ultrasound in experimental biology.Journal of General Physiology,1942,26:179
[2]Hickey RC,Fry WJ,et al.Human pituitary irradiation with focused ultrasound.Arch Surg,1961,83:620.
[3]王洪武.现代肿瘤靶向治疗技术.北京:中国医药科技出版社.2005
[4]Beerlage HP,Thuroff S,Madersbacher S,et al.Current status of minimally invasive treatment options for localized prostate carcinoma.Eur Urol,2000,37(1):2-13.
[5]Beerlage HP,Thuroff S,Debruyne FM,et al.Transrectal High Intensity Focused Ultrasound Using the Ablatherm Device in the Treatment of Localized Prostate Carcinoma[J].Urology,1999,54(2):273-277.
[6]Chaussy CG,Thuroff S.High Intensive Focused Ultrasound in Localized Prostate Cancer.J Endourol,2000,14(3):293-299
[7]Chapelon J Y,Ribault M,Vernier F,et al.Treatment of Localised Prostate Cancer with Transrectal High Intensity Focused Ultrasound.Eur J Ultrasound,1999,9(1):31-38.
[8]Chen LL,Haar GR,chritopher RH.Influence of ablated tissue on the formation of high intensity focused ultrasound lesions.Ultrasound Med Biol,1997,21:921-931.
[9]Yang R,Reilly CR,Rescorla FJ,et al.High Intensity Focused Ultrasound in the Treatment of Experimental Liver Cancer[J].Arch Surg,1991,126(8):1002-1009.
[10]Adams JB,Moore RG,Anderson JH,et al.High Intensity Focused Ultrasound Ablation of Rabbit Kidney Tumors.J Endourol,1996,10(1):71-75.
[11]Hill CR,Haar GR.Review Article:High Intensity Focused Ultrasound Potential for Cancer Treatment.Br J Radiol,1995,68(816):1296-1303.
[12]冯若,刘中奇,姚锦钟,等.超声诊断设备原理与设计 北京:中国医药科技出版社,1993.
[13]胡洁,李苏宜,姜藻.高强度聚焦超声治疗恶性肿瘤进展.肿瘤学杂 志.2003,2:103-106
[14]吴琴,曹有德,武烽,等.HIFU对兔VX2乳腺肿瘤毛细淋巴管的破坏效应.临床超声医学杂志,2002,4:1
[15]吴刚,杨永光,方玉华,等.高强度聚焦超声及其联合甲氨蝶呤对人膀胱癌细胞的体外效应.重庆医学,2001,30:193
[16]赵洪,袁永熙,陆昌宜,等。高强度超声治疗乳腺癌后机体免疫功能的变化.中国超声诊断杂志,2003,4:427
[17]牛凤岐,朱承纲,程洋.国内HIFU产业持续发展必须夯实的几项技术基础.世界医疗器械,2004,10(4):86-89
[18]McNab A,Kirk K J,Cochran A.Ultrasonic transducers for high temperature applications.Science,Measurement and Technology,IEEE Proceedings,1998,145(5):229-236
[19]Pei J,Khuri-Yakub BT,Degertekin FL,et al.In situ simultaneous measurement of temperature and thin film thickness with ultrasonic techniques.1996IEEE Ultrasonics Symposium,1996,2:1039-1042
[20]Kil MK,Dong GJ,Jong HC,et al.A new measurement system of very high temperature in atomic pile using ultrasonic delay time.2001 IEEE Electrical and Electronic Technology Proceedings,2001,2:860-863
[21]孙崇正.超声波测温技术进展.宇航计测技术,1995,15(2):34-42
[22]Simon C,VanBaren P,Ebbini E.Quantitative Analysis and Applications of Non-Invasive Temperature Estimation using Diagnostic Ultrasound.1997 IEEE Ultrasonics Symposium,1319-1322
[23]Seip R,VanBaren P,Cain CA,et al.Noninvasive real-time multipoint temperature control for ultrasound phased array treatments.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 1996;43:1063-1073.
[24]Seip R,Ebbini ES.Invasive and non-invasive feedback for ultrasound phased array thermometry.1994 IEEE Ultrasonics Symposium,1994,3:1821-1824
[25]Seip R,Ebbini ES.Non-invasive monitoring of ultrasound phased array hyperthermia and surgery treatments.1997 IEEE Engineering in Medicine and Biology Society,1997,1:663-664
[26]卢莹,刘晓宙,龚秀芬,等.生物媒质中声学参量与温度的关系研究.声学技术,2003,22:39-40
[27]卢莹,刘晓宙,龚秀芬,等.生物介质中非线性声参量对温度依赖关系的研究.科学通报 2004,49(24):2517-1519
[28]Qian X.W.,Xiong L.L.,Yu J.S.et al.Noninvasive thermometer for HIFU and its scaling.Proceedings of Ultrasonics International(UI'05) and World Congress on Ultrasonics(WCU).2006,44(1):31-35
[29]Wear KA,Wagner RF,Insana MF,et al.Application of Autoregressive spectral analysis to cepstral estimation of mean scatterer spacing,1993 IEEE Trans on UFFC,1993,40(1):50-58
[30]Weng L,Reid JM,Shankar PM,et al.Nonuniform phase distribution in ultrasound speckle analysis.I.Background and experimental demonstration.1992IEEE Trans on UFFC,1992,39(3):352-359
[31]Weng L,Reid JM,Shankar PM,et al.Nonuniform phase distribution in ultrasound speckle analysis.Ⅱ.Parametric expression and a frequency sweeping technique to measure mean scatterer spacing.1992 IEEE Trans on UFFC,1992,39(3):360-365
[32]Weng L,Reid JM,Shankar OM,et al.Non-uniform ultrasound speckle phase distribution applied to scatterer spacing estimation,Proceedings of the IEEE Ultrasonics Symposium,1990,3:1593-1596
[33]Wang HZ,Yu HB,Lin FL,et al.Method and experiment of noninvasive temperature estimation by ultrasound echo pulses.1998 IEEE Engineering in Medicine and Biology Society,1998,3:1513-1516
[34]于洪斌,王鸿樟.反射超声无损测温.声学技术,1997,16(3):101-103
[35]于洪斌,王鸿樟.基于时频分析的反射超声无损测温新方法.上海交通大学学报,1999,33(10):1202-1205
[36]白净.医学超声成像原理.北京:清华大学出版社,1998
[37]Silverstein SD,Ultrasound scattering model:2-D cross-correlation and focusing criteria-theory,simulations,and experiments.2001 IEEE Trans.UFFC,2001,48(4):1023-1030.
[38]Insana MF,Wagner RF.Describing small-scale structure in random media using pulse-echo ultrasound.Journal of the Acoustical Society of America,1990,87(1):179
[39]重庆医科大学医学超声工程研究所.现代超声学研究——冯若教授七 秩华诞纪念文集.江苏:南京大学出版社,2002.138-142
[40]牛金海,朱贻盛.肿瘤热疗中的超声无损测温技术.生物医学工程学杂志,2000:17(2):202-205
[41]Mia RS,Loew MH,Wear KA,et al.Mean scatterer spacing estimation using the complex cepstrum.Proc SPIE-Int.Soc.OPt.Eng.(USA),1997,30-34
[42]徐泾平,李莉,吴延军,等.生物组织散射元平均间距估计的一种新方法.生物物理学报,1996,12(4):653-662
[43]他得安,陈启敏,刘镇清.基于子波变换及其“WD倒谱”法的肾组织超声散射微结构特征分析.中国生物医学工程学报.2000,19(4):361-367.
[44]牛金海,张红煊,王鸿章,等.基于离散随机介质平均散射声功率的无损测温方法.声学学报,2001,26(3):247-251
[45]Pernot M,Tanter M,Berco J,et al.Temperature estimation using ultrasonic spatial compound imaging.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 2004;51:606-615.
[46]Simon C,VanBaren P,Ebbini E.Two-dimensional temperature estimation using diagnostic ultrasound.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 1998;45:1088-1099.
[47]Varghese T,Zagzebski JA,Chen Q,Techavipoo U,Frank G,Johnson C,Wright A,Lee FT Jr.Ultrasound monitoring of temperature change during radiofrequency ablation:preliminary in-vivo results.Ultrasound in Medicine &Biology 2002;28:321-329.
[48]Seip R,VanBaren P,Simon C,et al.Non-invasive spatio-temporal temperature estimation using diagnostic ultrasound.1995 IEEE Ultrasonics Symposium,1995,2:1613-1616
[49]Miller N.R.,Bamber J.C..Ultrasonic measurement of the temperature distribution due to absorption of diagnostic ultrasound:potential and limitations.Journal of Physics:Conference Series 1,2004,128-133.
[50]Sun ZG,"A multi-gate time-of-flight technique for estimation of temperature distribution in heated tissue theory and computer simulation",Ultrasonics 1999
[51]R.M.Arthur,W.L.Straube,J.W.Trobaugh et al.Non-invasive estimation of hyperthermia temperatures with ultrasound.Int.J.Hyperthermia,September 2005; 21(6):pp.589-600
[52]Zhigang Sun,Hao Ying.A Multi-Gate Time-of-Flight Technique for Estimation of Temperature Distribution in Heated Tissue:Theory and Computer Simulation,Ultrasonica,1999,37:107-122.
[53]Peter J.K.,Ajay A..Temperature Rise Measured Noninvasively During Thermal Therapy Using Backscattered Ultrasound.2004 IEEE International Ultrasonics,Ferroelectrics,and Frequency Control Joint 50th Anniversary Conference.720-723
[54]Sehgal CM,Brown GM,Bahn RC,et al.Measurement and use of acoustic nonlinearity and sound speed to estimate composition of excised livers.Ultrasound in Med&Biol,1986,12(11):865
[55]Straube WL,Martin AR.Theoretical estimation of the temperature dependence of backscattered ultrasonic power for noninvasive thermometry.Ultrasound in Med.& Biol.,1994,20(9):915
[56]Behari J,Singh AK.Temperature Dependence of Speed of Propagation in Soft Tissues and the Effect of Fixation at Ultrasonic Frequencies.1992 IEEE Engineering in Medicine and Biology Society,1992,5:2108-2109
[57]Seip R,Ebbini E.S.Noninvasive Estimation of Tissue Temperature Response to Heating Fields Diagnostic Ultrasound.1995 IEEE Trans.BME,1995,42(8):828-839
[58]Ueno S I,Furuya N,Fukukita H,et al.Application of nonlinear ultrasonic pulse reflection method -Measurement of acoustic parameters.Japanese Journal of Applied Physics,1989,28(suppl):191-193.
[59]夏雅琴,吴水才,等.用双超声脉冲法测量生物介质的非线性参数B/A.北京生物医学工程,2001,20(4):265-267
[60]吴水才,夏雅琴,贾丽芹,等.测量人体温度的双脉冲超声波传感系统.传感器技术.2001,20(4):6-8
[61]宗孔德.数字信号处理—理论算法与实现.清华大学出版社,1997
[62]Christakis A.Damianou,et al.,Dependence of ultrasonic attenuation and absorption in dog soft tissues on temperature and thermal dose,J.Acoust.Soc.Am.,1997;102(1):628-634.
[63]R.L.Clarke,N.L.Bush,G.R.Ter Haar,The changes in acoustic attenuation due to in vitro heating, Ultrasound in Med. & Biol., 2003; 29(1):127-135.
[64] Zderic V, Keshavarzi A, Andrew, MA et al, Attenuation of porcine tissues in vivo after high intensity ultrasound treatment, Ultrasound in Med. & Biol., 2004; 30(1): 61-66.
[65] Peter D, Bevan , Michael D, Sherar, B-scan ultrasound imaging of thermal coagulation in bovine liver: frequency shift attenuation mapping, Ultrasound in Med. & Biol., 2001,27(6):809-817.
[66] Peter D, Bevan , Michael D, Sherar, B-scan ultrasound imaging of thermal coagulation in bovine liver: log envelope slope attenuation mapping, Ultrasound in Med. & Biol, 2001; 27(3):379-387.
[67] Yao H, Phukpattaranont P, Ebbini E S. Detection and mapping of thermal lesions using dual-modeultrasound phased arrays. IEEE ultrasonics symposium Proceedings. 2002:1435-1438.
[68] Stafford R J,Kallel F,Price RE,et al. Elastographic imaging of thermal lesions in soft tissue:A preliminary study in vitro, Ultrasound in Medicine and Biology, 1998,24 (9):1449-1458
[69] Chen S.S, Wright N.T., Humphrey J.D.. Heat-induced changes in the mechanics of a collagenous tissue: isothermal, isotonic shrinkage. Journal of Biomechanical Engineering ,1998, 382-388
[70] Kallel F, Stafford RJ, Price RE, et al, The feasibility of elastographic visualization of hifu-induced thermal lesions in soft tissues, Ultrasound in Med. & Biol., 1999, 25(4):641-647.
[71] Righetti R, Kallel F, Stafford R J, et al, Elastographic characterization of hifu-induced lesions in canine livers, Ultrasound in Med. & Biol, 1999, 25(7):1099-1113.
[72] Re'mi Souchon, Olivier Rouvie're, et al. Visualisation of hifu lesions using elastography of the human prostate in vivo: preliminary results, Ultrasound in Med. & Biol, 2003, 29(7):1007-1015.
[73] Kevin J. Parker, Dongshan Fu, et al. Vibration sonoelastography and the detectability of lesions, Ultrasound in Med. & Biol, 1998,24(9): 1437-1447.
[74] Taylor, L.S, Wang, P, Rubens, D.J. et al. Three dimensional in-vitro thermal lesion monitoring using vibration sonoelastography, Ultrasound in Medicine and Biology 2003,29(5),Supplement:S57
[75]钟徽,万明习.高强度聚焦超声(HIFU)治疗监控成像及评价的研究现状.世界医疗器械,2004,10(11):22-24.
[76]王琳,秦叔逵,李桂梅,等.高强度聚焦超声定位损伤幼猪肌肉组织的即时和延迟反应.实用临床医药杂志,2006,10(5):1-3
[77]李华林,李冰,张丰明,等.高强度聚焦超声与射频治疗子宫肌瘤术中超声监控.临床超声医学杂志,2003,5(6):347-348.
[78]周永健,聂玉强,李瑜元,等.超声用于评价高强度聚焦超声联合射频治疗大肝癌的研究.临床超声医学杂志,2006,8(2):92-94
[79]邹建中,伍烽,李佩希,等。超声监控超声聚焦刀治疗肝癌及疗效评价研究.中国超声医学杂志,2000,16(10):766-768
[80]J.Seo,B.C.Tran,T.L.Hall,et al.Evaluation of Ultrasound Tissue Damage Based on Changes in Image Echogenicity.Proceedings of the 2002 IEEE Ultrasonics Symposium,2002,1399-1402
[81]Wu S.C.,Ren X.Y.,Bai Y.P.,et al.A Study on the Temperature Correlation of B-mode Ultrasonic Image Gray for Noninvasive Temperature Monitoring in Hyperthermia.Chinese Journal of Biomedical Engineering,2006,15(1):26-30
[82]任新颖,吴水才,杜旭,等.组织B型超声图像灰度与其温度相关性研究.北京生物医学工程,2004,23(2):116-119
[83]Youssef A.,Van K.G..,Zuna,I,et al.Noninvasive temperature estimation of ultrasound texturalparameters.,Proceedings of the Annual International Conference of the IEEE Engineering,1989,413-414.
[84]吴凝,阳维,陈磊,等.射频消融组织的B超图像纹理分析.中国医学影像技术,2007,23(1):140-143
[85]周爱华,郑应平,王令群.医学数据挖掘综述.中华医学实践杂志,2005,2(4)
[86]朱明.数据挖掘.合肥:中国科学技术大学出版社,2002,5.
[87]Han JW,Kamber M著 范明 孟小峰译.数据挖掘:概念与技术.机械工业出版社,2001
[88]Krzysztof J.Cios,William Moore.Uniqueness of medical data mining.Artificial Intelligenee in Medicine,2002,26:1-24.
[89]朱凌云,吴宝明.医学数据挖掘的技术、方法及应用.生物医学工程学杂志,2003,3(20):559-562.
[90]朱玉全,杨鹤标,孙蕾:数据挖掘技术 东南大学出版社 2006 184-186
[91]余辉,吕扬生.数据挖掘技术在生物医学领域的应用.国外医学·生物医学工程分册,2003,2(26):54-59.
[92]Castleman KR.Digital Image Processing.New Jersey:Prentice Hall,1996.
[93]W.Pratt,O.D.Faugeras,A.Gagalowicz.Visual discrimination of stochastic texture fields.IEEE Trans.on System,Man and Cybernetics.SMC-8,1978,796-804,
[94]J.Sklansky.Image segmentation and feature extraction,SMC-8,1978,237-247
[95]H.Tarmura,S.Mori,T.Yamawaki.Textured features corresponding to Cybernetics,SMC-8,1978,460-473.
[96]Haralick RM.Statistical and Structural Approaches to Texture[J].Proceeding of IEEE,1975,67(5):786-804.
[97]Castleman KP.Digital image Processing(影印本),北京:清华大学出版社,1998:498-510
[98]任俊,黄丹丹,李志能.结合纹理分析和支撑矢量机的汽车牌照定位研究.浙江大学学报,2006,40(8):1352-1357
[99]汪小毅,林江莉,李德玉,等.基于纹理分析的脂肪肝B超图像识别.航天医学与医学工程,2004,17(2):144-148
[100]李朝阳,徐涛.基于灰度共生矩阵的肝纤维化B超图像特征提取.杭州师范学院学报:医学版,2007,27(2):105-106,124
[101]郭秀花,张勇,王瓛等.应用Sandwich Logistic回归分析探讨肺小结节恶性病变影响因素.数理医药学杂志,2008,21(1):66-68
[102]林瑶,田捷,何晖光.一种交互式的医学纹理图像分割方法.软件学报,2002,13(4):1-6
[103]姚宇华,严洪,熊江辉.基于灰度共生矩阵特征的对模拟微重力条件下心肌细胞骨架的图像分析.航天医学与医学工程,2003,16(6):422-425
[104]Mandelbrot B.B..Fractals:Form,Chance and Dimension.San Francisco,CA:Fressman,1977.
[105]Mandelbrot B.B.Fractal geometry of nature,freeman,San Fran cisco,1982
[106]Pentland A.P.Fractal-Based Description of Natural Scenes.IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):661-674.
[107]Pentland A.P.Shading into texture.Artificial Intelligence,1986,29:147-170.
[108]Mandelbrot B.B.,The Fractal Geometry of Nature,W.H.Freeman,New York,1983
[109]刘昉,程敬之.肝脏超声图像分形特性的研究.西安交通大学学报,1999,33(5):21-23
[110]汪家旺,于立燕,王德杭.肺癌分形维数特征的研究.中国医学物理学杂志,2005,22(1):393-395
[111]K.J.Falconer.The geometry of fractal sets.Cambridge University Press,1985.
[112]康晓东.医学图像信号变换与压缩.北京:清华大学出版社,2004
[113]法尔科内.分形几何-数学基础及其应用.沈阳:东北大学出版社.2001.
[114]Sarkar N,Chaudhur IBB.An efficient app roach to estimate fractal dimension of texture images.Pattern Recognition,1992,25(9):1035-1041.
[115]Sarkar N,Chaudhur IBB.An efficient differential box-counting app roach to compute fractal dimension of image IEEE Transactions System Man and Cybernet.1994,24(1):115-120.
[116]聂书君,童忠勇,于春水等.多维分形维数分析多发性硬化患者表现正常脑白质.首都医科大学学报,2007,28(3):359-362
[117]陈真诚,周兆英,于前等.人体肝脏组织CT图像的分维特征研究.航天医学与医学工程,2005,18(3):206-210
[118]Gonzalez RC,Woods RE.著.阮秋琦,阮宇智等译.数字图像处理(第二版).北京:电子工业出版社.2003
[119]Mallat S.A Compact Multiresolution Representation:The Wavelet Model.Proc.IEEE Computer Soiety Workshop on Computer Vision,IEEE Computer Press,Washington,D.C.,2-7
[120]张贤达.现代信号处理.北京:清华大学出版社,2002
[121]Mallat SG.A theory for Multiresolution signal decomposition:The wavelet representation.IEEE Trans on Pattern Analysis Machine Intelligence.1989,11(7):674-693
[122]Ojala T,Pietik(a|¨)inen M,Harwood D.A comparative study of texture measures with classification based on feature distributions.Pattern Recognition,1996,29(1):51-59.
[123]王积分,赵增华,舒炎泰.小波分析用于菌落图像的纹理分割.计算机研究和发展,1999,36(12):237-242
[124]Semler,L,Dettori,L,Furst,J.Wavelet-Based Texture Classification of Tissues in Computed Tomography.Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems,2005,265-270
[125]邰晓英,王李冬,巴特尔.基于小波纹理、语义特征和相关反馈的医学图像检索.电路与系统学报,2007,12(4):24-30
[126]周平,李传富,陈荻等.一种基于小波变换的配准特征点自动标记算法.北京生物医学工程,2007,26(4):356-359
[127]蒋庆琅[著],方积乾等[译].实用统计分析方法.北京:北京医科大学中国协和医科大学联合出版社,1998
[128]Hotelling H Analysis of a complex of statistical variables into principal components.Journal of Educational Psychology,1933,24:417-441
[129]阮祥燕,杜永洪,孔繁斌,等.高强度聚焦超声定位损伤香猪肝脏组织的病理转归研究.中华实验外科杂志,1999,16(3):263-264
[130]刘长安,白晋,伍烽,等.高强度聚焦超声对人乳腺癌组织定位损伤的初步观察.中华超声影像学杂志,1998,7(4):239-241.
[131]耿兰溪,秦叔逵,王琳,等.高强度聚焦超声在治疗晚期复发转移浅表肿瘤中的应用.东南国防医药,2004,6(3):186-187
[132]冯若,王智彪.实用超声治疗学 北京:科学技术文献出版社,2002
[133]田志东,朱明程.一种全搜索块匹配算法的FPGA实现及其应用.深圳大学学报理工,2006,23(2):133-136
[134]李庆扬,王能超,等.数值分析.武汉:华中理工大学出版社.1982,101-106.
[135]Gibson,JJ The Perception of the Visual World,Riverside Press,Cambridge,England.1950
[136]Horn B.,Schunck B.Determining optical flow.Artificial Intelligence,1981,17:185-203.
[137]Barton JL,Fleet DJ.,Beauchemin S,et al Performance of optical flow techniques.IEEE Conference on Computer Vision and Pattern Recognition, Champaign,1992,236-242
[138]Fleet DJ,Jepson AD.Computation of component image velocity from local phase information.International Journal of Computer Vision,1990,5(1):77-104
[139]柴作朋,辜嘉,舒华忠,等.心脏序列图象光流场运动的相关算法.中国图象图形学报A,2003 8(z1):800-804
[140]Loncaric,S,Majcenic,Z.Optical flow algorithm for cardiac motion estimation Engineering in Medicine and Biology Society,2000.Proceedings of the 22nd Annual International Conference of the IEEE Volume 1,Issue,2000,1:415-417
[141]Cooper DH,Madsen BR,Graham J.Estimating Motion in Ultrasound Images of the Small Bowel:Optical Flow without Image Structure.SCIA03,2003,571-578.
[142]谢洪波,黄家祥,徐智,等.基于光流法的冠状动脉造影图像序列中血管运动的估计.工程图学学报,2003,24(3):83-88
[143]Keeling S,Ring W.Medical Image Registration and Interpolation by Optical Flow with Maximal Rigidity Journal of Mathematical Imaging and Vision,2005,23(1):47-65
[144]T.Cover,P.Hart,"Nearest neighbor pattern classification," IEEE Trans.Information Theory IT-13,1967,21-27
[145]C.J.C.Burges,A tutorial on support vector machines for pattern recognition.Data mining and knowledge discovery,1998,2:121-167.
[146]Vapnik,V.:The Nature of Statistical Learning Theory.Springer-Verlag,New York.(1995).
[147]Vapnik V.统计学习理论的本质 张学工,译.北京:清华大学出版社,2000.91-108.
[148]Hsu CW,Lin C.J..A Comparison of Methods for Multi-class Support Vector Machines.IEEE Transactions on Neural Networks,2002,13:415-425.
[149]Westton J,Watkins C.Multi-class support vector machines.Proceedings of ESANN99,Brussels,1999,D.Factor Press
[150]Knerr,S.,Personnaz,L.,Dreyfus,G.Single-layer learning revisited:A stepwise procedure for building and training neural network.Neurocomputing: Algorithms,Architectures and Applications,NATO ASI,Berlin:Springer-Verlag.1990
[151]Bottou,L.,Cortes,C.,Denker,J.S.et.al.Comparison of classifier methods:a case study in handwritten digit recognition,ICPR94,1994,B:77-87.
[152]范良志,罗飞,喻道远,等.高强度聚焦超声肿瘤治疗机开发中的关键技术.中国医疗器械杂志,2005,29(2):115-119.
[153]冯军,高远,王庆才.高强度聚焦超声对复发性后腹膜肉瘤的疗效及其与超声影像变化的关系研究.苏州大学学报(医学版),2007,27(2):256-257
[154]严加勇,庄天戈.医学超声图像分割技术的研究及发展趋势.北京生物医学工程,2003,22(1):67-71
[155]T.Kohonen.Self-organized formation of topologically correct feature maps.Biological Cybemetics,1982,43:59-69,
[156]Choi DI,Park SH.Self-Creating and Organizing Neural Networks.IEEE Trans.Neural Networks.1994,.5(4):561-575
[157]李波.医学超声图像分割技术.中国医学物理学杂志,2005,22(4):572-573,566
[158]Widrow B.The rubber mask technique,Parts Ⅰ and Parts Ⅱ.Pattern Recognition.1973,5:175-211.
[159]Fischler M,Elschlager R.The representation and matching of pictorial structure.IEEE Transaction On Computer.1973,22(1):67-92.
[160]Kass M,Witkin A,Terzopoulos D.Snakes:active contour models.Proceedings of First Internatianal Conference on Computer Vision,London,England,1987,259-268.
[161]Li CM,Liu J,Fox MD.Segmentation of external force field for automatic initialization and splitting of snakes.Pattern Recognition,2005,38:1947-1960.
[162]Osher S,Sethian J.Fronts Propagating with curvature dependent speed:algorithms based on Hamilton-jacobi formulations.Comp.Phys.1988,79:12-49
[163]Sumengen B,Mnajunath BS,Kenney C.Image segmentation using curve evolution.Proc.of the Conf.on Record of the 35th Asilomar Conference on Signals,Systems and Computers,2001,2:1141-1145.
[164]Tsai A,Yezzi A,Willsky AS.Curve evolution implementation of the mumford-shah functional for image segmentation,denoising,interpolation,and magnification. IEEE Trans, on Image Processing, 2001,10(8): 1169-1186.
[165] Chan TF, Vese LA. Active contours without edges. IEEE Trans, on Image Processing, 2001,10(2):266-277.
[166] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int'l Journal of Computer Vision, 1997,22(1):61-79.
[167] Paragios N, Deriche R. Geodesic active regions for supervised texture segmentation. In: Proc. of the Int'l Conf. on Computer Vision. 1999. 926-932.
[168] Paragios N, Deriche R. Geodesic active regions for motion estimation and tracking. In: Proc. of the IEEE Int'l Conf. on Computer Vision. Kerkyra: IEEE Computer Society, 1999. 688-674.
[169] Mumford D, Shah J. Boundary detection by minimizing functions. Proceedings of Conf Computer Vision and Pattern Recognition, 1985,41-44.
[170] Giorgi E De, Carriero M, Leaci A. Existence theorem for a minimum problem with free discontinuity set. Archive for Rational Mechanics and Analysis, 1989,108:195-218
[171] Gobbino M. Finite difference approximation of the Mumford -Shah functional. Communication Pure Applied Mathematics, 1998,51:197-228
[172] Chambolle A. Image segmentation by variational methods: Mumford-Shah functional and the discrete appromations. SIAM Jounal of Applied Mathematics,1995(55):827-863.
[173] Black A, Zisserman A. Visual reconstruction. MIT Press, Cambridge, MA, 1987.
[174] Chambolle A. Finite-difference discretizations of the Mumford-Shah functional. Mathenatics Modeling and Numerical Analysis. 1999, 33(2):261-288
[175] Ambrosio L, Tortorelli V. Approximation of functionals depending on jumps by elliptic functionals via (?)-convergence. Communication on Pure and Applied Mathematics, 1990,43: 999-1036
[176] Braides A, Dal MG. Non local approximation of the Mumford-Shah functional. Calculus Variation and Partial Differential Equations, 1997, 5(4):293-322.
[177] Finzi Vita S. et al. Some numerical experiments on the variational approach to image segmentation. Proc. of the Second European Workshop on Image Processing and Mean Curvature Motion. Palma de Maillorca 1995:233-240.
[178]Richardson TJ,Mitter S K.A variational formulation based edge focusing algorithm.Sadhana Acad.P.Eng.S.1999,22:553-574
[179]Blaise B.Image segmentation with a finite element method.Mathematics Modeling and Numerical Analysis.1999,33(2):229-244.
[180]Cilarlet P.The finite element method for elliptic problems.In Studies in Mathematics and its Application.Amsterdam,The North-Holland.1987,4.
[181]Chan T,Vese LA.A level set algorithm for minimizing the Mumford—Shah functional in image processing.IEEE Workshop on Variational and Level Set Methods.Vancouver,CA:,2001:161-168.
[182]Sher S,Sethian J A.Fronts propagating with curvature dependent speed:algorithms based on Hamilton-Jacobi formulation.Journal of Computer Physics,1988,79(1):12-49
[183]陈健,田捷,薛健,等.多速度函数水平集算法及在医学分割中的应用.软件学报.2007,18(4):842-849
[184]刁现芬,陈思平墚长虹,等.基于阈值区间的水平集算法在耳蜗分割中的应用.浙江大学学报(工学版),2006,40(2):262-266
[185]朱付平,田捷,林瑶,等.基于Level Set方法的医学图像分割.软件学报,2002,13(9):1866-1871
[186]Evans L.C.Partial Differential Equations.Providence.Rhode Island:Ameriean Mathematical Society,1998
[2]Hickey RC,Fry WJ,et al.Human pituitary irradiation with focused ultrasound.Arch Surg,1961,83:620.
[3]王洪武.现代肿瘤靶向治疗技术.北京:中国医药科技出版社.2005
[4]Beerlage HP,Thuroff S,Madersbacher S,et al.Current status of minimally invasive treatment options for localized prostate carcinoma.Eur Urol,2000,37(1):2-13.
[5]Beerlage HP,Thuroff S,Debruyne FM,et al.Transrectal High Intensity Focused Ultrasound Using the Ablatherm Device in the Treatment of Localized Prostate Carcinoma[J].Urology,1999,54(2):273-277.
[6]Chaussy CG,Thuroff S.High Intensive Focused Ultrasound in Localized Prostate Cancer.J Endourol,2000,14(3):293-299
[7]Chapelon J Y,Ribault M,Vernier F,et al.Treatment of Localised Prostate Cancer with Transrectal High Intensity Focused Ultrasound.Eur J Ultrasound,1999,9(1):31-38.
[8]Chen LL,Haar GR,chritopher RH.Influence of ablated tissue on the formation of high intensity focused ultrasound lesions.Ultrasound Med Biol,1997,21:921-931.
[9]Yang R,Reilly CR,Rescorla FJ,et al.High Intensity Focused Ultrasound in the Treatment of Experimental Liver Cancer[J].Arch Surg,1991,126(8):1002-1009.
[10]Adams JB,Moore RG,Anderson JH,et al.High Intensity Focused Ultrasound Ablation of Rabbit Kidney Tumors.J Endourol,1996,10(1):71-75.
[11]Hill CR,Haar GR.Review Article:High Intensity Focused Ultrasound Potential for Cancer Treatment.Br J Radiol,1995,68(816):1296-1303.
[12]冯若,刘中奇,姚锦钟,等.超声诊断设备原理与设计 北京:中国医药科技出版社,1993.
[13]胡洁,李苏宜,姜藻.高强度聚焦超声治疗恶性肿瘤进展.肿瘤学杂 志.2003,2:103-106
[14]吴琴,曹有德,武烽,等.HIFU对兔VX2乳腺肿瘤毛细淋巴管的破坏效应.临床超声医学杂志,2002,4:1
[15]吴刚,杨永光,方玉华,等.高强度聚焦超声及其联合甲氨蝶呤对人膀胱癌细胞的体外效应.重庆医学,2001,30:193
[16]赵洪,袁永熙,陆昌宜,等。高强度超声治疗乳腺癌后机体免疫功能的变化.中国超声诊断杂志,2003,4:427
[17]牛凤岐,朱承纲,程洋.国内HIFU产业持续发展必须夯实的几项技术基础.世界医疗器械,2004,10(4):86-89
[18]McNab A,Kirk K J,Cochran A.Ultrasonic transducers for high temperature applications.Science,Measurement and Technology,IEEE Proceedings,1998,145(5):229-236
[19]Pei J,Khuri-Yakub BT,Degertekin FL,et al.In situ simultaneous measurement of temperature and thin film thickness with ultrasonic techniques.1996IEEE Ultrasonics Symposium,1996,2:1039-1042
[20]Kil MK,Dong GJ,Jong HC,et al.A new measurement system of very high temperature in atomic pile using ultrasonic delay time.2001 IEEE Electrical and Electronic Technology Proceedings,2001,2:860-863
[21]孙崇正.超声波测温技术进展.宇航计测技术,1995,15(2):34-42
[22]Simon C,VanBaren P,Ebbini E.Quantitative Analysis and Applications of Non-Invasive Temperature Estimation using Diagnostic Ultrasound.1997 IEEE Ultrasonics Symposium,1319-1322
[23]Seip R,VanBaren P,Cain CA,et al.Noninvasive real-time multipoint temperature control for ultrasound phased array treatments.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 1996;43:1063-1073.
[24]Seip R,Ebbini ES.Invasive and non-invasive feedback for ultrasound phased array thermometry.1994 IEEE Ultrasonics Symposium,1994,3:1821-1824
[25]Seip R,Ebbini ES.Non-invasive monitoring of ultrasound phased array hyperthermia and surgery treatments.1997 IEEE Engineering in Medicine and Biology Society,1997,1:663-664
[26]卢莹,刘晓宙,龚秀芬,等.生物媒质中声学参量与温度的关系研究.声学技术,2003,22:39-40
[27]卢莹,刘晓宙,龚秀芬,等.生物介质中非线性声参量对温度依赖关系的研究.科学通报 2004,49(24):2517-1519
[28]Qian X.W.,Xiong L.L.,Yu J.S.et al.Noninvasive thermometer for HIFU and its scaling.Proceedings of Ultrasonics International(UI'05) and World Congress on Ultrasonics(WCU).2006,44(1):31-35
[29]Wear KA,Wagner RF,Insana MF,et al.Application of Autoregressive spectral analysis to cepstral estimation of mean scatterer spacing,1993 IEEE Trans on UFFC,1993,40(1):50-58
[30]Weng L,Reid JM,Shankar PM,et al.Nonuniform phase distribution in ultrasound speckle analysis.I.Background and experimental demonstration.1992IEEE Trans on UFFC,1992,39(3):352-359
[31]Weng L,Reid JM,Shankar PM,et al.Nonuniform phase distribution in ultrasound speckle analysis.Ⅱ.Parametric expression and a frequency sweeping technique to measure mean scatterer spacing.1992 IEEE Trans on UFFC,1992,39(3):360-365
[32]Weng L,Reid JM,Shankar OM,et al.Non-uniform ultrasound speckle phase distribution applied to scatterer spacing estimation,Proceedings of the IEEE Ultrasonics Symposium,1990,3:1593-1596
[33]Wang HZ,Yu HB,Lin FL,et al.Method and experiment of noninvasive temperature estimation by ultrasound echo pulses.1998 IEEE Engineering in Medicine and Biology Society,1998,3:1513-1516
[34]于洪斌,王鸿樟.反射超声无损测温.声学技术,1997,16(3):101-103
[35]于洪斌,王鸿樟.基于时频分析的反射超声无损测温新方法.上海交通大学学报,1999,33(10):1202-1205
[36]白净.医学超声成像原理.北京:清华大学出版社,1998
[37]Silverstein SD,Ultrasound scattering model:2-D cross-correlation and focusing criteria-theory,simulations,and experiments.2001 IEEE Trans.UFFC,2001,48(4):1023-1030.
[38]Insana MF,Wagner RF.Describing small-scale structure in random media using pulse-echo ultrasound.Journal of the Acoustical Society of America,1990,87(1):179
[39]重庆医科大学医学超声工程研究所.现代超声学研究——冯若教授七 秩华诞纪念文集.江苏:南京大学出版社,2002.138-142
[40]牛金海,朱贻盛.肿瘤热疗中的超声无损测温技术.生物医学工程学杂志,2000:17(2):202-205
[41]Mia RS,Loew MH,Wear KA,et al.Mean scatterer spacing estimation using the complex cepstrum.Proc SPIE-Int.Soc.OPt.Eng.(USA),1997,30-34
[42]徐泾平,李莉,吴延军,等.生物组织散射元平均间距估计的一种新方法.生物物理学报,1996,12(4):653-662
[43]他得安,陈启敏,刘镇清.基于子波变换及其“WD倒谱”法的肾组织超声散射微结构特征分析.中国生物医学工程学报.2000,19(4):361-367.
[44]牛金海,张红煊,王鸿章,等.基于离散随机介质平均散射声功率的无损测温方法.声学学报,2001,26(3):247-251
[45]Pernot M,Tanter M,Berco J,et al.Temperature estimation using ultrasonic spatial compound imaging.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 2004;51:606-615.
[46]Simon C,VanBaren P,Ebbini E.Two-dimensional temperature estimation using diagnostic ultrasound.IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control 1998;45:1088-1099.
[47]Varghese T,Zagzebski JA,Chen Q,Techavipoo U,Frank G,Johnson C,Wright A,Lee FT Jr.Ultrasound monitoring of temperature change during radiofrequency ablation:preliminary in-vivo results.Ultrasound in Medicine &Biology 2002;28:321-329.
[48]Seip R,VanBaren P,Simon C,et al.Non-invasive spatio-temporal temperature estimation using diagnostic ultrasound.1995 IEEE Ultrasonics Symposium,1995,2:1613-1616
[49]Miller N.R.,Bamber J.C..Ultrasonic measurement of the temperature distribution due to absorption of diagnostic ultrasound:potential and limitations.Journal of Physics:Conference Series 1,2004,128-133.
[50]Sun ZG,"A multi-gate time-of-flight technique for estimation of temperature distribution in heated tissue theory and computer simulation",Ultrasonics 1999
[51]R.M.Arthur,W.L.Straube,J.W.Trobaugh et al.Non-invasive estimation of hyperthermia temperatures with ultrasound.Int.J.Hyperthermia,September 2005; 21(6):pp.589-600
[52]Zhigang Sun,Hao Ying.A Multi-Gate Time-of-Flight Technique for Estimation of Temperature Distribution in Heated Tissue:Theory and Computer Simulation,Ultrasonica,1999,37:107-122.
[53]Peter J.K.,Ajay A..Temperature Rise Measured Noninvasively During Thermal Therapy Using Backscattered Ultrasound.2004 IEEE International Ultrasonics,Ferroelectrics,and Frequency Control Joint 50th Anniversary Conference.720-723
[54]Sehgal CM,Brown GM,Bahn RC,et al.Measurement and use of acoustic nonlinearity and sound speed to estimate composition of excised livers.Ultrasound in Med&Biol,1986,12(11):865
[55]Straube WL,Martin AR.Theoretical estimation of the temperature dependence of backscattered ultrasonic power for noninvasive thermometry.Ultrasound in Med.& Biol.,1994,20(9):915
[56]Behari J,Singh AK.Temperature Dependence of Speed of Propagation in Soft Tissues and the Effect of Fixation at Ultrasonic Frequencies.1992 IEEE Engineering in Medicine and Biology Society,1992,5:2108-2109
[57]Seip R,Ebbini E.S.Noninvasive Estimation of Tissue Temperature Response to Heating Fields Diagnostic Ultrasound.1995 IEEE Trans.BME,1995,42(8):828-839
[58]Ueno S I,Furuya N,Fukukita H,et al.Application of nonlinear ultrasonic pulse reflection method -Measurement of acoustic parameters.Japanese Journal of Applied Physics,1989,28(suppl):191-193.
[59]夏雅琴,吴水才,等.用双超声脉冲法测量生物介质的非线性参数B/A.北京生物医学工程,2001,20(4):265-267
[60]吴水才,夏雅琴,贾丽芹,等.测量人体温度的双脉冲超声波传感系统.传感器技术.2001,20(4):6-8
[61]宗孔德.数字信号处理—理论算法与实现.清华大学出版社,1997
[62]Christakis A.Damianou,et al.,Dependence of ultrasonic attenuation and absorption in dog soft tissues on temperature and thermal dose,J.Acoust.Soc.Am.,1997;102(1):628-634.
[63]R.L.Clarke,N.L.Bush,G.R.Ter Haar,The changes in acoustic attenuation due to in vitro heating, Ultrasound in Med. & Biol., 2003; 29(1):127-135.
[64] Zderic V, Keshavarzi A, Andrew, MA et al, Attenuation of porcine tissues in vivo after high intensity ultrasound treatment, Ultrasound in Med. & Biol., 2004; 30(1): 61-66.
[65] Peter D, Bevan , Michael D, Sherar, B-scan ultrasound imaging of thermal coagulation in bovine liver: frequency shift attenuation mapping, Ultrasound in Med. & Biol., 2001,27(6):809-817.
[66] Peter D, Bevan , Michael D, Sherar, B-scan ultrasound imaging of thermal coagulation in bovine liver: log envelope slope attenuation mapping, Ultrasound in Med. & Biol, 2001; 27(3):379-387.
[67] Yao H, Phukpattaranont P, Ebbini E S. Detection and mapping of thermal lesions using dual-modeultrasound phased arrays. IEEE ultrasonics symposium Proceedings. 2002:1435-1438.
[68] Stafford R J,Kallel F,Price RE,et al. Elastographic imaging of thermal lesions in soft tissue:A preliminary study in vitro, Ultrasound in Medicine and Biology, 1998,24 (9):1449-1458
[69] Chen S.S, Wright N.T., Humphrey J.D.. Heat-induced changes in the mechanics of a collagenous tissue: isothermal, isotonic shrinkage. Journal of Biomechanical Engineering ,1998, 382-388
[70] Kallel F, Stafford RJ, Price RE, et al, The feasibility of elastographic visualization of hifu-induced thermal lesions in soft tissues, Ultrasound in Med. & Biol., 1999, 25(4):641-647.
[71] Righetti R, Kallel F, Stafford R J, et al, Elastographic characterization of hifu-induced lesions in canine livers, Ultrasound in Med. & Biol, 1999, 25(7):1099-1113.
[72] Re'mi Souchon, Olivier Rouvie're, et al. Visualisation of hifu lesions using elastography of the human prostate in vivo: preliminary results, Ultrasound in Med. & Biol, 2003, 29(7):1007-1015.
[73] Kevin J. Parker, Dongshan Fu, et al. Vibration sonoelastography and the detectability of lesions, Ultrasound in Med. & Biol, 1998,24(9): 1437-1447.
[74] Taylor, L.S, Wang, P, Rubens, D.J. et al. Three dimensional in-vitro thermal lesion monitoring using vibration sonoelastography, Ultrasound in Medicine and Biology 2003,29(5),Supplement:S57
[75]钟徽,万明习.高强度聚焦超声(HIFU)治疗监控成像及评价的研究现状.世界医疗器械,2004,10(11):22-24.
[76]王琳,秦叔逵,李桂梅,等.高强度聚焦超声定位损伤幼猪肌肉组织的即时和延迟反应.实用临床医药杂志,2006,10(5):1-3
[77]李华林,李冰,张丰明,等.高强度聚焦超声与射频治疗子宫肌瘤术中超声监控.临床超声医学杂志,2003,5(6):347-348.
[78]周永健,聂玉强,李瑜元,等.超声用于评价高强度聚焦超声联合射频治疗大肝癌的研究.临床超声医学杂志,2006,8(2):92-94
[79]邹建中,伍烽,李佩希,等。超声监控超声聚焦刀治疗肝癌及疗效评价研究.中国超声医学杂志,2000,16(10):766-768
[80]J.Seo,B.C.Tran,T.L.Hall,et al.Evaluation of Ultrasound Tissue Damage Based on Changes in Image Echogenicity.Proceedings of the 2002 IEEE Ultrasonics Symposium,2002,1399-1402
[81]Wu S.C.,Ren X.Y.,Bai Y.P.,et al.A Study on the Temperature Correlation of B-mode Ultrasonic Image Gray for Noninvasive Temperature Monitoring in Hyperthermia.Chinese Journal of Biomedical Engineering,2006,15(1):26-30
[82]任新颖,吴水才,杜旭,等.组织B型超声图像灰度与其温度相关性研究.北京生物医学工程,2004,23(2):116-119
[83]Youssef A.,Van K.G..,Zuna,I,et al.Noninvasive temperature estimation of ultrasound texturalparameters.,Proceedings of the Annual International Conference of the IEEE Engineering,1989,413-414.
[84]吴凝,阳维,陈磊,等.射频消融组织的B超图像纹理分析.中国医学影像技术,2007,23(1):140-143
[85]周爱华,郑应平,王令群.医学数据挖掘综述.中华医学实践杂志,2005,2(4)
[86]朱明.数据挖掘.合肥:中国科学技术大学出版社,2002,5.
[87]Han JW,Kamber M著 范明 孟小峰译.数据挖掘:概念与技术.机械工业出版社,2001
[88]Krzysztof J.Cios,William Moore.Uniqueness of medical data mining.Artificial Intelligenee in Medicine,2002,26:1-24.
[89]朱凌云,吴宝明.医学数据挖掘的技术、方法及应用.生物医学工程学杂志,2003,3(20):559-562.
[90]朱玉全,杨鹤标,孙蕾:数据挖掘技术 东南大学出版社 2006 184-186
[91]余辉,吕扬生.数据挖掘技术在生物医学领域的应用.国外医学·生物医学工程分册,2003,2(26):54-59.
[92]Castleman KR.Digital Image Processing.New Jersey:Prentice Hall,1996.
[93]W.Pratt,O.D.Faugeras,A.Gagalowicz.Visual discrimination of stochastic texture fields.IEEE Trans.on System,Man and Cybernetics.SMC-8,1978,796-804,
[94]J.Sklansky.Image segmentation and feature extraction,SMC-8,1978,237-247
[95]H.Tarmura,S.Mori,T.Yamawaki.Textured features corresponding to Cybernetics,SMC-8,1978,460-473.
[96]Haralick RM.Statistical and Structural Approaches to Texture[J].Proceeding of IEEE,1975,67(5):786-804.
[97]Castleman KP.Digital image Processing(影印本),北京:清华大学出版社,1998:498-510
[98]任俊,黄丹丹,李志能.结合纹理分析和支撑矢量机的汽车牌照定位研究.浙江大学学报,2006,40(8):1352-1357
[99]汪小毅,林江莉,李德玉,等.基于纹理分析的脂肪肝B超图像识别.航天医学与医学工程,2004,17(2):144-148
[100]李朝阳,徐涛.基于灰度共生矩阵的肝纤维化B超图像特征提取.杭州师范学院学报:医学版,2007,27(2):105-106,124
[101]郭秀花,张勇,王瓛等.应用Sandwich Logistic回归分析探讨肺小结节恶性病变影响因素.数理医药学杂志,2008,21(1):66-68
[102]林瑶,田捷,何晖光.一种交互式的医学纹理图像分割方法.软件学报,2002,13(4):1-6
[103]姚宇华,严洪,熊江辉.基于灰度共生矩阵特征的对模拟微重力条件下心肌细胞骨架的图像分析.航天医学与医学工程,2003,16(6):422-425
[104]Mandelbrot B.B..Fractals:Form,Chance and Dimension.San Francisco,CA:Fressman,1977.
[105]Mandelbrot B.B.Fractal geometry of nature,freeman,San Fran cisco,1982
[106]Pentland A.P.Fractal-Based Description of Natural Scenes.IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):661-674.
[107]Pentland A.P.Shading into texture.Artificial Intelligence,1986,29:147-170.
[108]Mandelbrot B.B.,The Fractal Geometry of Nature,W.H.Freeman,New York,1983
[109]刘昉,程敬之.肝脏超声图像分形特性的研究.西安交通大学学报,1999,33(5):21-23
[110]汪家旺,于立燕,王德杭.肺癌分形维数特征的研究.中国医学物理学杂志,2005,22(1):393-395
[111]K.J.Falconer.The geometry of fractal sets.Cambridge University Press,1985.
[112]康晓东.医学图像信号变换与压缩.北京:清华大学出版社,2004
[113]法尔科内.分形几何-数学基础及其应用.沈阳:东北大学出版社.2001.
[114]Sarkar N,Chaudhur IBB.An efficient app roach to estimate fractal dimension of texture images.Pattern Recognition,1992,25(9):1035-1041.
[115]Sarkar N,Chaudhur IBB.An efficient differential box-counting app roach to compute fractal dimension of image IEEE Transactions System Man and Cybernet.1994,24(1):115-120.
[116]聂书君,童忠勇,于春水等.多维分形维数分析多发性硬化患者表现正常脑白质.首都医科大学学报,2007,28(3):359-362
[117]陈真诚,周兆英,于前等.人体肝脏组织CT图像的分维特征研究.航天医学与医学工程,2005,18(3):206-210
[118]Gonzalez RC,Woods RE.著.阮秋琦,阮宇智等译.数字图像处理(第二版).北京:电子工业出版社.2003
[119]Mallat S.A Compact Multiresolution Representation:The Wavelet Model.Proc.IEEE Computer Soiety Workshop on Computer Vision,IEEE Computer Press,Washington,D.C.,2-7
[120]张贤达.现代信号处理.北京:清华大学出版社,2002
[121]Mallat SG.A theory for Multiresolution signal decomposition:The wavelet representation.IEEE Trans on Pattern Analysis Machine Intelligence.1989,11(7):674-693
[122]Ojala T,Pietik(a|¨)inen M,Harwood D.A comparative study of texture measures with classification based on feature distributions.Pattern Recognition,1996,29(1):51-59.
[123]王积分,赵增华,舒炎泰.小波分析用于菌落图像的纹理分割.计算机研究和发展,1999,36(12):237-242
[124]Semler,L,Dettori,L,Furst,J.Wavelet-Based Texture Classification of Tissues in Computed Tomography.Proceedings of the 18th IEEE International Symposium on Computer-Based Medical Systems,2005,265-270
[125]邰晓英,王李冬,巴特尔.基于小波纹理、语义特征和相关反馈的医学图像检索.电路与系统学报,2007,12(4):24-30
[126]周平,李传富,陈荻等.一种基于小波变换的配准特征点自动标记算法.北京生物医学工程,2007,26(4):356-359
[127]蒋庆琅[著],方积乾等[译].实用统计分析方法.北京:北京医科大学中国协和医科大学联合出版社,1998
[128]Hotelling H Analysis of a complex of statistical variables into principal components.Journal of Educational Psychology,1933,24:417-441
[129]阮祥燕,杜永洪,孔繁斌,等.高强度聚焦超声定位损伤香猪肝脏组织的病理转归研究.中华实验外科杂志,1999,16(3):263-264
[130]刘长安,白晋,伍烽,等.高强度聚焦超声对人乳腺癌组织定位损伤的初步观察.中华超声影像学杂志,1998,7(4):239-241.
[131]耿兰溪,秦叔逵,王琳,等.高强度聚焦超声在治疗晚期复发转移浅表肿瘤中的应用.东南国防医药,2004,6(3):186-187
[132]冯若,王智彪.实用超声治疗学 北京:科学技术文献出版社,2002
[133]田志东,朱明程.一种全搜索块匹配算法的FPGA实现及其应用.深圳大学学报理工,2006,23(2):133-136
[134]李庆扬,王能超,等.数值分析.武汉:华中理工大学出版社.1982,101-106.
[135]Gibson,JJ The Perception of the Visual World,Riverside Press,Cambridge,England.1950
[136]Horn B.,Schunck B.Determining optical flow.Artificial Intelligence,1981,17:185-203.
[137]Barton JL,Fleet DJ.,Beauchemin S,et al Performance of optical flow techniques.IEEE Conference on Computer Vision and Pattern Recognition, Champaign,1992,236-242
[138]Fleet DJ,Jepson AD.Computation of component image velocity from local phase information.International Journal of Computer Vision,1990,5(1):77-104
[139]柴作朋,辜嘉,舒华忠,等.心脏序列图象光流场运动的相关算法.中国图象图形学报A,2003 8(z1):800-804
[140]Loncaric,S,Majcenic,Z.Optical flow algorithm for cardiac motion estimation Engineering in Medicine and Biology Society,2000.Proceedings of the 22nd Annual International Conference of the IEEE Volume 1,Issue,2000,1:415-417
[141]Cooper DH,Madsen BR,Graham J.Estimating Motion in Ultrasound Images of the Small Bowel:Optical Flow without Image Structure.SCIA03,2003,571-578.
[142]谢洪波,黄家祥,徐智,等.基于光流法的冠状动脉造影图像序列中血管运动的估计.工程图学学报,2003,24(3):83-88
[143]Keeling S,Ring W.Medical Image Registration and Interpolation by Optical Flow with Maximal Rigidity Journal of Mathematical Imaging and Vision,2005,23(1):47-65
[144]T.Cover,P.Hart,"Nearest neighbor pattern classification," IEEE Trans.Information Theory IT-13,1967,21-27
[145]C.J.C.Burges,A tutorial on support vector machines for pattern recognition.Data mining and knowledge discovery,1998,2:121-167.
[146]Vapnik,V.:The Nature of Statistical Learning Theory.Springer-Verlag,New York.(1995).
[147]Vapnik V.统计学习理论的本质 张学工,译.北京:清华大学出版社,2000.91-108.
[148]Hsu CW,Lin C.J..A Comparison of Methods for Multi-class Support Vector Machines.IEEE Transactions on Neural Networks,2002,13:415-425.
[149]Westton J,Watkins C.Multi-class support vector machines.Proceedings of ESANN99,Brussels,1999,D.Factor Press
[150]Knerr,S.,Personnaz,L.,Dreyfus,G.Single-layer learning revisited:A stepwise procedure for building and training neural network.Neurocomputing: Algorithms,Architectures and Applications,NATO ASI,Berlin:Springer-Verlag.1990
[151]Bottou,L.,Cortes,C.,Denker,J.S.et.al.Comparison of classifier methods:a case study in handwritten digit recognition,ICPR94,1994,B:77-87.
[152]范良志,罗飞,喻道远,等.高强度聚焦超声肿瘤治疗机开发中的关键技术.中国医疗器械杂志,2005,29(2):115-119.
[153]冯军,高远,王庆才.高强度聚焦超声对复发性后腹膜肉瘤的疗效及其与超声影像变化的关系研究.苏州大学学报(医学版),2007,27(2):256-257
[154]严加勇,庄天戈.医学超声图像分割技术的研究及发展趋势.北京生物医学工程,2003,22(1):67-71
[155]T.Kohonen.Self-organized formation of topologically correct feature maps.Biological Cybemetics,1982,43:59-69,
[156]Choi DI,Park SH.Self-Creating and Organizing Neural Networks.IEEE Trans.Neural Networks.1994,.5(4):561-575
[157]李波.医学超声图像分割技术.中国医学物理学杂志,2005,22(4):572-573,566
[158]Widrow B.The rubber mask technique,Parts Ⅰ and Parts Ⅱ.Pattern Recognition.1973,5:175-211.
[159]Fischler M,Elschlager R.The representation and matching of pictorial structure.IEEE Transaction On Computer.1973,22(1):67-92.
[160]Kass M,Witkin A,Terzopoulos D.Snakes:active contour models.Proceedings of First Internatianal Conference on Computer Vision,London,England,1987,259-268.
[161]Li CM,Liu J,Fox MD.Segmentation of external force field for automatic initialization and splitting of snakes.Pattern Recognition,2005,38:1947-1960.
[162]Osher S,Sethian J.Fronts Propagating with curvature dependent speed:algorithms based on Hamilton-jacobi formulations.Comp.Phys.1988,79:12-49
[163]Sumengen B,Mnajunath BS,Kenney C.Image segmentation using curve evolution.Proc.of the Conf.on Record of the 35th Asilomar Conference on Signals,Systems and Computers,2001,2:1141-1145.
[164]Tsai A,Yezzi A,Willsky AS.Curve evolution implementation of the mumford-shah functional for image segmentation,denoising,interpolation,and magnification. IEEE Trans, on Image Processing, 2001,10(8): 1169-1186.
[165] Chan TF, Vese LA. Active contours without edges. IEEE Trans, on Image Processing, 2001,10(2):266-277.
[166] Caselles V, Kimmel R, Sapiro G. Geodesic active contours. Int'l Journal of Computer Vision, 1997,22(1):61-79.
[167] Paragios N, Deriche R. Geodesic active regions for supervised texture segmentation. In: Proc. of the Int'l Conf. on Computer Vision. 1999. 926-932.
[168] Paragios N, Deriche R. Geodesic active regions for motion estimation and tracking. In: Proc. of the IEEE Int'l Conf. on Computer Vision. Kerkyra: IEEE Computer Society, 1999. 688-674.
[169] Mumford D, Shah J. Boundary detection by minimizing functions. Proceedings of Conf Computer Vision and Pattern Recognition, 1985,41-44.
[170] Giorgi E De, Carriero M, Leaci A. Existence theorem for a minimum problem with free discontinuity set. Archive for Rational Mechanics and Analysis, 1989,108:195-218
[171] Gobbino M. Finite difference approximation of the Mumford -Shah functional. Communication Pure Applied Mathematics, 1998,51:197-228
[172] Chambolle A. Image segmentation by variational methods: Mumford-Shah functional and the discrete appromations. SIAM Jounal of Applied Mathematics,1995(55):827-863.
[173] Black A, Zisserman A. Visual reconstruction. MIT Press, Cambridge, MA, 1987.
[174] Chambolle A. Finite-difference discretizations of the Mumford-Shah functional. Mathenatics Modeling and Numerical Analysis. 1999, 33(2):261-288
[175] Ambrosio L, Tortorelli V. Approximation of functionals depending on jumps by elliptic functionals via (?)-convergence. Communication on Pure and Applied Mathematics, 1990,43: 999-1036
[176] Braides A, Dal MG. Non local approximation of the Mumford-Shah functional. Calculus Variation and Partial Differential Equations, 1997, 5(4):293-322.
[177] Finzi Vita S. et al. Some numerical experiments on the variational approach to image segmentation. Proc. of the Second European Workshop on Image Processing and Mean Curvature Motion. Palma de Maillorca 1995:233-240.
[178]Richardson TJ,Mitter S K.A variational formulation based edge focusing algorithm.Sadhana Acad.P.Eng.S.1999,22:553-574
[179]Blaise B.Image segmentation with a finite element method.Mathematics Modeling and Numerical Analysis.1999,33(2):229-244.
[180]Cilarlet P.The finite element method for elliptic problems.In Studies in Mathematics and its Application.Amsterdam,The North-Holland.1987,4.
[181]Chan T,Vese LA.A level set algorithm for minimizing the Mumford—Shah functional in image processing.IEEE Workshop on Variational and Level Set Methods.Vancouver,CA:,2001:161-168.
[182]Sher S,Sethian J A.Fronts propagating with curvature dependent speed:algorithms based on Hamilton-Jacobi formulation.Journal of Computer Physics,1988,79(1):12-49
[183]陈健,田捷,薛健,等.多速度函数水平集算法及在医学分割中的应用.软件学报.2007,18(4):842-849
[184]刁现芬,陈思平墚长虹,等.基于阈值区间的水平集算法在耳蜗分割中的应用.浙江大学学报(工学版),2006,40(2):262-266
[185]朱付平,田捷,林瑶,等.基于Level Set方法的医学图像分割.软件学报,2002,13(9):1866-1871
[186]Evans L.C.Partial Differential Equations.Providence.Rhode Island:Ameriean Mathematical Society,1998