公开(公告)号：US06850635B2

公开(公告)日：2005-02-01

申请号：US09965414

申请日：2001-09-27

Applicant: Olivier Gerard ,  Milena Planells-Rodriguez ,  Pierre Lelong ,  Sherif Makram-Ebeid

Inventor： Olivier Gerard ,  Milena Planells-Rodriguez ,  Pierre Lelong ,  Sherif Makram-Ebeid

IPC: A61B6/00 ,  G06F19/00 ,  G06T1/00 ,  G06K9/00

CPC classification number: G06F19/321 ,  G06F19/00 ,  G16H50/50 ,  Y10S128/922

Abstract: The invention relates to an image processing method of extracting geometrical data of the spine, for extracting the left and right pedicle landmarks of each spine vertebra, comprising steps of: acquiring image data of a 2-D frontal image of the spine; associating spine States to vertebra positions along the spine and estimating locations of left and right pedicle landmark Candidates in each State; defining a State Cost for forming Couples of left and right pedicle landmark Candidates (PL and PR); estimating sets of Best Couple Candidates, in each State, from the lowest State Costs; defining a Path Cost to go from one State to the next State; selecting a pedicle landmark Couple in each spine State (V) among the Best Couple Candidates from the minimum Path Costs, and localizing the left and right pedicle landmarks of each spine vertebra from said selected pedicle landmark Couple. The invention also relates to a system, a medical apparatus and a program product for carrying out the method.Application: Medical Imaging x-ray Medical System and apparatus; Program Product for Medical Imaging.

Abstract translation: 本发明涉及一种提取脊柱几何数据的图像处理方法，用于提取每个脊椎椎弓根的左右椎弓根标志，其特征在于包括以下步骤：获取脊柱的二维正面图像的图像数据; 将脊柱状态与沿着脊柱的椎骨位置相关联，并估计每个国家左侧和右侧椎弓根标志物候选者的位置; 确定形成左右椎弓根标志性候选人（PL和PR）夫妇的国家费用; 估计每个国家的最佳夫妇候选人的最低国家费用; 确定从一个国家到下一个国家的路径成本; 从最小路径成本中选择最佳夫妇候选者中的每个脊柱状态（V）中的椎弓根标志物，并且从所述选定的椎弓根标记物对本地化每个脊椎的左右椎弓根标记。本发明还涉及一种系统， 一种用于执行该方法的医疗设备和程序产品。应用：医疗成像x射线医疗系统和装置; 医学成像程序产品。

公开(公告)号：US5830141A

公开(公告)日：1998-11-03

申请号：US721914

申请日：1996-09-27

Applicant: Sherif Makram-Ebeid ,  Jean-Pierre Roux ,  Eric Cohen-Solal

Inventor： Sherif Makram-Ebeid ,  Jean-Pierre Roux ,  Eric Cohen-Solal

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  A61B5/05

CPC classification number: G06T7/0012 ,  G06T2207/10116 ,  G06T2207/20084 ,  G06T2207/30068

Abstract: An image processing method for automatic detection of regions of a predetermined type of cancer in an intensity image of a part of a human or animal body, includes, for a number of points of a part of said image, the determination of a set (referred to as a vector) of components formed by characteristic values derived from the intensity distribution around each point in said part of the image, and the use of a classification system for determining the probability of the point associated with said vector belonging to a region of said part of the image which corresponds to the predetermined type of cancer or to another region. An image processing device for carrying out such a method is utilized in conjunction with a digital medical imaging system.

Abstract translation: 一种用于对人或动物身体的一部分的强度图像中的预定类型的癌症的区域进行自动检测的图像处理方法包括：对于所述图像的一部分的多个点，确定一组 通过从图像的所述部分中的每个点周围的强度分布导出的特征值形成的分量的分量系统，以及使用分类系统来确定与属于所述图像的区域的所述矢量相关联的点的概率的分类系统 对应于预定类型的癌症或另一区域的图像的一部分。 与数字医学成像系统结合使用用于执行这种方法的图像处理装置。

公开(公告)号：US5469530A

公开(公告)日：1995-11-21

申请号：US887636

申请日：1992-05-22

Applicant: Sherif Makram-Ebeid

Inventor： Sherif Makram-Ebeid

IPC: G06F15/18 ,  G06G7/60 ,  G06N3/00 ,  G06N3/063 ,  G06N99/00

CPC classification number: G06K9/622 ,  G06N3/063

Abstract: Unsupervised training method for a neural net and a neural net classifier device wherein test vectors are supplied to the neural net whose operational parameters are classified in a stochastic labeling procedure by mutually correlating the net's output activations for each test vector and on the basis thereof generating weighting factors that scale the probabilities when selecting a class at random. Once the test vectors are thus classified, the operational parameters of the net are modified in order to intensify the differences among the patterns of output activations for all test vectors.

Abstract translation: 用于神经网络和神经网络分类器装置的无监督训练方法，其中将测试向量提供给神经网络，神经网络的操作参数通过相互关联每个测试向量的网络的输出激活而在随机标签过程中分类，并且基于其产生加权 随机选择课程时可能性的因素。 一旦测试向量被分类，则修改网络的操作参数，以加强所有测试向量的输出激活模式之间的差异。

公开(公告)号：US4489480A

公开(公告)日：1984-12-25

申请号：US480445

申请日：1983-03-30

Applicant: Gerard M. Martin ,  Sherif Makram-Ebeid ,  Camille Venger

Inventor： Gerard M. Martin ,  Sherif Makram-Ebeid ,  Camille Venger

IPC: H01L29/80 ,  H01L21/265 ,  H01L21/338 ,  H01L29/10 ,  H01L29/207 ,  H01L29/812 ,  H01L21/263 ,  H01L27/12

CPC classification number: H01L29/1029 ,  H01L21/2654 ,  H01L21/2656 ,  H01L29/207 ,  H01L29/812 ,  Y10S438/919

Abstract: The invention relates to a method of manufacturing field effect transistors of gallium arsenide obtained by ion implantation of light donors, such as silicon or selenium, in a semi-insulating substrate of gallium arsenide. In order to reduce out-diffusion of the deep level (EL.sub.2) responsible for parasitic phenomena in the operation of the transistors, the method is characterized in that in addition oxygen ions are implanted in at least the region of the substrate intended to form the channel region of the field effect transistor. After implantation, the substrate is sintered at a temperature between 600.degree. and 900.degree. C. in either an enveloping substance or uncovered, and/or in an atmosphere of arsine.

Abstract translation: 本发明涉及一种通过在砷化镓的半绝缘衬底中离子注入诸如硅或硒之类的供体的砷化镓的场效应晶体管的方法。 为了减少在晶体管的操作中负责寄生现象的深层（EL2）的扩散，该方法的特征在于，除了氧离子至少注入到用于形成沟道的衬底区域外 场效应晶体管的区域。 植入后，将基材在包封物质或未包覆的和/或胂气氛中在600-900℃的温度下烧结。