公开(公告)号：AU679601B2

公开(公告)日：1997-07-03

申请号：AU1290395

申请日：1994-11-30

Applicant: ARCH DEV CORP

Inventor： GIGER MARYELLEN L ,  CHEN CHIN-TU ,  ARMATO SAMUEL ,  DOI KUNIO

IPC: G01N23/04 ,  A61B6/00 ,  G06T1/00 ,  G06T3/00 ,  G06T5/00 ,  G06T7/60 ,  G06K9/32 ,  G06F19/00 ,  G06F159/00 ,  G06K9/38

公开(公告)号：CA2326776C

公开(公告)日：2008-06-17

申请号：CA2326776

申请日：1999-04-02

Applicant: ARCH DEV CORP

Inventor： DOI KUNIO ,  KATSURAGAWA SHIGEHIKO ,  ISHIDA TAKAYUKI

IPC: A61B6/00 ,  G06T7/00 ,  G01N20060101 ,  G06K9/00 ,  G06K9/80 ,  G06T1/00 ,  G06T3/00 ,  G06T5/40 ,  G06T5/50 ,  G06T7/20 ,  H04N5/325

Abstract: A method of computerized analysis of temporally sequential digital images, including (a) determining first shift values between pixels of a first digital image and corresponding pixels of a second digital image; (b) warping the second digital image based on the first shift values to obtain a first warped image in which spatial locations of pixels are varied in relation to the first shift values; (c) determining second shift values between pixels of the first digital image and pixels of the first warped image; and (d) warping the first warped image based on the second shift values to obtain a second warped image in which spatial locations of pixels of the first warped image are varied in relation to the second shift values. Additional iterations of image warping are possible to enhance image registration between the first digital image and the warped version of the second digital image, followed by image subtraction of the first digital image and the final warped image to produce a difference image from which diagnosis of temporal changes ensues.

公开(公告)号：DE69432641D1

公开(公告)日：2003-06-12

申请号：DE69432641

申请日：1994-11-29

Applicant: ARCH DEV CORP

Inventor： GIGER L ,  DOI KUNIO

IPC: A61B6/00 ,  G06F19/00 ,  G06T7/00 ,  A61B5/103

Abstract: A computerized method and system for the radiographic analysis of bone structure and risk of future fracture with or without the measurement of bone mass. Techniques include texture analysis for use in quantitating the bone structure and risk of future fracture. The texture analysis of the bone structure incorporates directionality information, for example in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in the bony region of interest. The system also includes using dual energy imaging in order to obtain measures of both mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artificial neural networks in order to yield a likelihood of risk of future fracture. In addition, a method and system is presented in which dual-energy imaging techniques are used to yield measures of both bone mass and bone structure with one low-dose radiographic examination; thus, making the system desirable for screening (for osteoporosis and risk of future fracture).

公开(公告)号：DE69432601D1

公开(公告)日：2003-06-05

申请号：DE69432601

申请日：1994-11-29

Applicant: ARCH DEV CORP

Inventor： GIGER L ,  DOI KUNIO ,  LU PING ,  HUO ZHIMIN

IPC: A61B6/00 ,  G06K9/00 ,  G06T1/00 ,  G06T7/00 ,  G06T5/00 ,  G06K9/46 ,  G06K9/44 ,  G06K9/62 ,  G06K9/38

Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.

公开(公告)号：AU2115901A

公开(公告)日：2001-04-30

申请号：AU2115901

申请日：2000-10-20

Applicant: ARCH DEV CORP

Inventor： LI QIANG ,  KATSURAGAWA SHIGEHIKO ,  DOI KUNIO

IPC: A61B6/00 ,  G06K9/00 ,  G06T1/00 ,  G06T3/00 ,  G06T5/00 ,  G06T5/50 ,  G06T7/00 ,  G06T7/60

Abstract: A method, system and computer readable medium of computerized processing of chest images including obtaining digital first and second images of a chest and detecting rib edges in at least one of the first and second images. The rib edges are detected by correlating points in the at least one of the first and second images to plural rib edge models using a Hough transform to identify approximate rib edges in one of the images, and delineating actual rib edges derived from the identified approximate rib edges using a snake model. The method system and computer readable medium further include deriving the shift values using the actual rib edges and warping one of the first and second images to produce a warped image which is registered to the other of the first and second images based at least in part on the shift values.

公开(公告)号：AT193815T

公开(公告)日：2000-06-15

申请号：AT95914166

申请日：1995-03-30

Applicant: ARCH DEV CORP

Inventor： GIGER MARYELLEN L ,  BAE KYONGTAE TY ,  DOI KUNIO

IPC: A61B6/03 ,  G06T1/00 ,  G06T7/00 ,  G06F19/00

Abstract: A method and system for the automated detection of lesions in computed tomographic images, including generating image data from at least one selected portion of an object, for example, from CT images of the thorax. The image data are then analyzed in order to produce the boundary of the thorax. The image data within the thoracic boundary is then further analyzed to produce boundaries of the lung regions using predetermined criteria. Features within the lung regions are then extracted using multi-gray-level thresholding and correlation between resulting multi-level threshold images and between at least adjacent sections. Classification of the features as abnormal lesions or normal anatomic features is then performed using geometric features yielding a likelihood of being an abnormal lesion along with its location in either the 2-D image section or in the 3-D space of the object.

公开(公告)号：AU8579498A

公开(公告)日：1999-02-16

申请号：AU8579498

申请日：1998-07-24

Applicant: ARCH DEV CORP

Inventor： NISHIKAWA ROBERT M ,  JIANG YULEI ,  ASHIZAWA KAZUTO ,  DOI KUNIO

IPC: A61B6/00 ,  G06T1/00 ,  G06T7/00

Abstract: A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region. In the detecting method candidate abnormalities in each of a plurality of digitized medical images are located, regions around one or more of the located candidate abnormalities in each of a plurality of digitized medical images are generated, the plurality of digitized medical images annotated with respective regions and candidate abnormalities within the regions are displayed, and a first indicator (e.g., blue arrow) is superimposed over candidate abnormalities comprising of clusters and a second indicator (e.g., red arrow) is superimposed over candidate abnormalities comprising of masses. In a user modification mode, during classification, a user modifies the located candidate abnormalities, the determined regions, and/or the extracted features, so as to modify the extracted features applied to the classification technique and the displayed results, and, during detection, a user modifies the located candidate abnormalities, the determined regions, and the extracted features, so as to modify the displayed results.

公开(公告)号：AU687958B2

公开(公告)日：1998-03-05

申请号：AU1257095

申请日：1994-11-29

Applicant: ARCH DEV CORP

Inventor： GIGER MARYELLEN L ,  DOI KUNIO ,  LU PING ,  HUO ZHIMIN

IPC: A61B6/00 ,  G06K9/00 ,  G06T1/00 ,  G06T7/00 ,  G06K9/46 ,  G06K9/44 ,  G06K9/62 ,  G06K9/38

Abstract: A method and system for the automated detection and classification of masses in mammograms. These method and system include the performance of iterative, multi-level gray level thresholding, followed by a lesion extraction and feature extraction techniques for classifying true masses from false-positive masses and malignant masses from benign masses. The method and system provide improvements in the detection of masses include multi-gray-level thresholding of the processed images to increase sensitivity and accurate region growing and feature analysis to increase specificity. Novel improvements in the classification of masses include a cumulative edge gradient orientation histogram analysis relative to the radial angle of the pixels in question; i.e., either around the margin of the mass or within or around the mass in question. The classification of the mass leads to a likelihood of malignancy.

公开(公告)号：AU2127095A

公开(公告)日：1995-10-23

申请号：AU2127095

申请日：1995-03-30

Applicant: ARCH DEV CORP

Inventor： GIGER MARYELLEN L ,  BAE KYONGTAE TY ,  DOI KUNIO

IPC: A61B6/03 ,  G06T1/00 ,  G06T7/00

Abstract: A method and system for the automated detection of lesions in computed tomographic images, including generating image data from at least one selected portion of an object, for example, from CT images of the thorax. The image data are then analyzed in order to produce the boundary of the thorax. The image data within the thoracic boundary is then further analyzed to produce boundaries of the lung regions using predetermined criteria. Features within the lung regions are then extracted using multi-gray-level thresholding and correlation between resulting multi-level threshold images and between at least adjacent sections. Classification of the features as abnormal lesions or normal anatomic features is then performed using geometric features yielding a likelihood of being an abnormal lesion along with its location in either the 2-D image section or in the 3-D space of the object.

公开(公告)号：CA2177478A1

公开(公告)日：1995-06-01

申请号：CA2177478

申请日：1994-11-29

Applicant: ARCH DEV CORP

Inventor： GIGR MARYELLEN L ,  DOI KUNIO

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

Abstract: A computerized method and system for the radiographic analysis of bone structure. Techniques include texture analysis for use in quantitating the bone structure and risk of fracture. Texture analysis of the bone structure incorporates directionality information, for example, in terms of the angular dependence of the RMS variation and first moment of the power spectrum of a ROI in a bony region. The system includes using dual energy imaging to obtain measures of both mass and bone structure with one exam. Specific applications are given for the analysis of regions within the vertebral bodies on conventional spine radiographs. Techniques include novel features that characterize the power spectrum of the bone structure and allow extraction of directionality features with which to characterize the spatial distribution and thickness of the bone trabeculae. These features are then merged using artifical neural networks in order to yield a likelihood of risk of future fracture.