公开(公告)号：WO9942949A9

公开(公告)日：1999-11-04

申请号：PCT/US9903282

申请日：1999-02-22

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO

IPC: G06T5/50 ,  G06T7/20 ,  G06K9/00

CPC classification number: G06T5/50 ,  A61B6/027 ,  G06T7/254

Abstract: Method and system for detection of interval change in medical images. Three-dimensional images, such as previous and current section images (10 and 11) in CT scans, are obtained. An anatomic feature, such as lungs, is used to select sections containing lung by a gray-level thresholding technique (13). The section correspondence between the current and previous scans is determined automatically. The initial registration of the corresponding sections in the two scans is achieved by a rotation correction (14) and a cross-correlation (15) technique. A more accurate registration between the corresponding current and previous section images is achieved by local matching (17). A nonlinear warping process (18) which is also based on the cross-correlation technique is applied to the previous image to yield a warped image after the matching. The final subtracted section images (19) were derived by subtracting of the previous section images from the corresponding current section images. Interval changes such as a change in tumor size and a newly developed pleural effusion are enhanced significantly.

Abstract translation: 用于检测医学图像间隔变化的方法和系统。 获得三维图像，例如CT扫描中的先前和当前部分图像（10和11）。 使用解剖学特征，如肺，通过灰度阈值技术选择含有肺的部位（13）。 自动确定当前扫描和以前扫描之间的部分对应关系。 通过旋转校正（14）和互相关（15）技术来实现两次扫描中相应部分的初始配准。 通过局部匹配（17）实现相应的当前和前一个截面图像之间的更准确的配准。 还将基于互相关技术的非线性翘曲过程（18）应用于先前的图像，以在匹配之后产生翘曲图像。 通过从相应的当前部分图像中减去前一部分图像导出最终减法部分图像（19）。 肿瘤大小变化和新发胸腔积液等间期变化明显增强。

公开(公告)号：EP1025535A4

公开(公告)日：2002-02-13

申请号：EP99933560

申请日：1999-07-21

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO ,  MACMAHON HEBER

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  G06K9/00 ,  G06F19/00

CPC classification number: G06T7/0012

Abstract: A method, system and computer readable medium configured for computerized detection of lung abnormalities, including obtaining a standard digital chest image (10) and a soft-tissue digital chest image; generating a first difference image from the standard digital chest image (20) and a second difference image from the soft-tissue digital chest image; identifying candidate abnormalities in the first and second difference images; extracting from the standard digital chest image and the first difference image predetermined first features of each of the candidate abnormalities identified in the first difference image; extracting from the soft-tissue digital chest image and the second difference images predetermined second features of each of the candidate abnormalities identified in the second difference image; analyzing the extracted first features and the extracted second features to identify and eliminate false positive candidate abnormalities respectively corresponding thereto; applying extracted features from remaining candidate abnormalities derived respectively from the first and second difference images and remaining after the elimination of the false positive candidate abnormalities to respective artificial neural networks to eliminate further false positive candidate abnormalities by performing a logical OR operation of the candidate abnormalities derived respectively from the first and second difference images.

公开(公告)号：EP1082694A4

公开(公告)日：2007-08-08

申请号：EP99909494

申请日：1999-02-23

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  G06K9/00

CPC classification number: G06T7/0012

Abstract: An automated method and a computer storage medium storing instructions for executing the method, for analysis of image features in lung nodule detection in a digital chest radiographic image, including preprocessing the image to identify candidate nodules in the image (1200); establishing a region of interest (ROI) including a candidate nodule within the ROI (1210); performing image enhancement of the candidate nodule within the ROI (1220); obtaining a histogram of accumulated edge gradients as a function of radial angles within the ROI after performing the image enhancement (1230); and determining whether the candidate nodule is a false positive based on the obtained histogram (1240).

Abstract translation: 一种自动化方法和计算机存储介质，存储用于执行该方法的指令，用于分析数字胸部放射线照相图像中的肺结节检测中的图像特征，包括预处理该图像以识别图像中的候选结节（1200）; 在ROI（1210）内建立包括候选结节的感兴趣区域（ROI）; 执行ROI内的候选结节的图像增强（1220）; 在执行图像增强（1230）之后获得作为ROI内的径向角的函数的累积边缘梯度的直方图; 以及基于所获得的直方图确定候选结节是否为假阳性（1240）。

公开(公告)号：AU2767099A

公开(公告)日：1999-09-06

申请号：AU2767099

申请日：1999-02-22

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO

IPC: G06T5/50 ,  G06T7/20 ,  G06K9/00

公开(公告)号：AU4959599A

公开(公告)日：2000-02-14

申请号：AU4959599

申请日：1999-07-21

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO ,  MACMAHON HEBER

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  G06K9/00

公开(公告)号：AU2868599A

公开(公告)日：1999-09-06

申请号：AU2868599

申请日：1999-02-23

Applicant: ARCH DEV CORP

Inventor： XU XIN-WEI ,  DOI KUNIO

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00 ,  G06K9/00

Abstract: An automated method, and a computer storage medium storing instructions for executing the method, for analysis of image features in lung nodule detection in a chest radiographic image represented by digital data, including preprocessing the image to identify candidate nodules in the image; establishing a region of interest (ROI) including a candidate nodule identified in the preprocessing step; performing image enhancement of the candidate nodule within the ROI; obtaining a histogram of accumulated edge gradients as a function of radial angles withing the ROI after performing the image enhancement; and determining whether the candidate nodule is a false positive based on the obtained histogram. A 64x64-pixel region of interest (ROI) centered at the candidate location is used. The contrast of the ROI is improved by a two-dimensional background subtraction. A nodule shape matched filter is used for enhancement of the nodular pattern located in the central area of the ROI. Analysis of the histogram resulted in identification of seven features, including (1) a maximum histogram value, (2) a minimum histogram value, (3) a partial average value of the histogram, (4) a standard deviation of the histogram values near the radial axis, (5) a partial standard deviation of histogram values, (6) a width of the histogram including both sides from zero degrees of the radial angle, at a predetermined histogram value, and (7) a ratio of a maximum histogram value near the radial axis to a maximum histogram value in two predetermined outside ranges of the radial axis, useful for the identification and elimination of false positives.