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    GAZE TRACKING SYSTEM WITH CALIBRATION IMPROVEMENT, ACCURACY COMPENSATION, AND GAZE LOCALIZATION SMOOTHING
    1.
    发明申请
    GAZE TRACKING SYSTEM WITH CALIBRATION IMPROVEMENT, ACCURACY COMPENSATION, AND GAZE LOCALIZATION SMOOTHING 审中-公开

    公开(公告)号：US20170364149A1

    公开(公告)日：2017-12-21

    申请号：US15535747

    申请日：2015-12-07

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： KONGKUO LU ,  YUECHEN QIAN

    IPC: G06F3/01

    CPC classification number: G06F3/013 ,  A61B3/113

    Abstract: In a gaze-tracking system, localized error correction may be applied. The display area may be partitioned into error-correction zones, and each zone may have its own error correcting parameters or function. Calibration points may be defined for each of these zones, and the correction parameters or function within each zone may be dependent upon the observed errors as the user views each calibration point associated with the zone. In like manner, the function applied to smooth the determined gaze point to reduce noise may also be localized by basing its characteristics on the noise exhibited at each calibration point associated with defined noise-calibration zones. The error correcting function and/or the noise smoothing function may be selected based on the requirements of the particular application that receives the corrected and smoothed gaze location.

    METHOD AND SYSTEM FOR CALCULATING A DISPLACEMENT OF AN OBJECT OF INTEREST
    2.
    发明申请
    METHOD AND SYSTEM FOR CALCULATING A DISPLACEMENT OF AN OBJECT OF INTEREST 审中-公开

    公开(公告)号：US20170345158A1

    公开(公告)日：2017-11-30

    申请号：US15535763

    申请日：2015-12-11

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： JINGPING XU ,  KONGKUO LU ,  WILLIAM TAO SHI ,  CHRISTOPHER STEPHEN HALL

    IPC: G06T7/20 ,  G06T7/38

    CPC classification number: G06T7/20 ,  G06T7/248 ,  G06T7/38 ,  G06T2207/10016 ,  G06T2207/10132 ,  G06T2207/20081 ,  G06T2207/30004

    Abstract: The invention relates to a method of calculating a displacement of an object of interest comprising a step of calculating (101) a displacement model of said object of interest from adjacent images of a set of pre-acquired images of said object of interest, said displacement model reflects the position of said object of interest along the time. The method is characterized in that the method further comprises the following. A step of determining (102) a first sub-set of images (S1) from said set of pre acquired images within one periodical time cycle of said set of pre-acquired images on the basis of the displacement model. A first step of identifying (103) a second sub-set of images (S2) from newly-acquired images, wherein images in said second sub-set of images (S2) are consecutive and have the same most similar image in said first sub-set of images (S1), wherein a first set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S1), and wherein said most similar image has the largest similarity level in said first set of similarity levels. A first step of selecting (104) a given image in said second sub-set of images (S2) as a first reference image (I1). A second step of identifying (105) a third sub-set of images (S3) from said newly-acquired images, wherein images in said third sub-set of images (S3) are consecutive and have the same most similar image in said first sub-set of images (S1), wherein a set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S1), and wherein said most similar image has the largest similarity level in said set of similarity levels. A second step of selecting (106) a given image in said third sub-set of images (S3) as a second reference image (I2). A step of calculating (107) the displacement between said second reference image (I2) and said first reference image (I1). The invention also relates to a corresponding system of displacement calculation.

    SELF-AWARE IMAGE SEGMENTATION METHODS AND SYSTEMS
    4.
    发明申请
    SELF-AWARE IMAGE SEGMENTATION METHODS AND SYSTEMS 审中-公开

    公开(公告)号：US20200151882A1

    公开(公告)日：2020-05-14

    申请号：US16737144

    申请日：2020-01-08

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： PINGKUN YAN ,  CHRISTOPHER STEPHEN HALL ,  KONGKUO LU

    IPC: G06T7/13 ,  G06T7/00 ,  G06T7/143 ,  G06T7/136 ,  G06T7/10

    Abstract: The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.

    OPTIMIZED ANATOMICAL STRUCTURE OF INTEREST LABELLING
    6.
    发明申请
    OPTIMIZED ANATOMICAL STRUCTURE OF INTEREST LABELLING 审中-公开

    公开(公告)号：US20170372007A1

    公开(公告)日：2017-12-28

    申请号：US15524322

    申请日：2015-10-22

    Applicant: KONINKLIJKE PHILIPS N.V.

    Inventor： KONGKUO LU ,  ALEXANDRA GROTH ,  YUECHEN QIAN ,  AXEL SAALBACH ,  RANJITH NAVEEN TELLIS ,  DANIEL BYSTROV ,  RAN COHEN ,  BELA FADIDA ,  LIOR WOLLOCH

    IPC: G06F19/00 ,  G06K9/46 ,  G06F17/30 ,  G06K9/66 ,  G06F17/27

    CPC classification number: G06F19/321 ,  G06F16/5866 ,  G06F17/2785 ,  G06K9/4676 ,  G06K9/66

    Abstract: The present application describes a system (100) and method for detecting and labeling structures of interest. The system includes a current patient study database (102) containing a current patient study (200) with clinical contextual information (706). The system also includes an image metadata processing engine (118) configured to extract metadata for preparing an input for an anatomical structure classifier (608), a natural language processing engine (120) configured to extract clinical context information (706) from the prior patient documents, an anatomical structure detection and labeling engine (718), and a display device (108) configured to display findings from the current patient study. The anatomical structure detection and labeling engine (718) is configured to identify and label one or more structures of interest (716) from the extracted metadata and clinical context information (706). The processor (112) is also configured to aggregate series level data. The method detects, label and prioritize anatomical structures (710). Specifically, once patient information is received from the current patient study (108), the labeled anatomical structures (710) and the high risk anatomical structures (714) are combined to form an optimized prioritized list of structures of interest (716).

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