James H Pike

US Patent:

20110274327, Nov 10, 2011

Filed:

Jun 24, 2011

Appl. No.:

13/168614

Inventors:

Jeffrey C. Wehnes - Richardson TX, US
James H. Pike - Carrollton TX, US
James P. Monaco - Piscataway NJ, US

Assignee:

VuCOMP, Inc. - Plano TX

International Classification:

G06K 9/34
G06K 9/00

US Classification:

382128, 382173

Abstract:

An image segmentation embodiment comprises applying a second derivative operator to the pixels of an image, growing a set of contours using seeding grid points as potential contour starting points, determining a contour strength vector for each of the contour pixels, generating a partial ellipse representing an estimated location of an object in the image, dividing the partial ellipse into a plurality of support sectors with control points, determining a contour strength and position for each contour, adjusting a position of each sector control point based on the contour positions weighted by the contour strengths of the contours centered in the respective sector, fitting the partial ellipse to the adjusted positions of the control points, and generating a segmentation mask of the object based on the partial fitted ellipse.

US Patent:

20110280465, Nov 17, 2011

Filed:

Jun 24, 2011

Appl. No.:

13/168588

Inventors:

Jeffrey C. Wehnes - Richardson TX, US
James H. Pike - Carrollton TX, US
James P. Monaco - Piscataway NJ, US

Assignee:

VuCOMP, Inc. - Plano TX

International Classification:

G06K 9/34

US Classification:

382132

Abstract:

An image segmentation embodiment comprises generating a start model comprising a set of model points approximating an outline of an object in an initial image, smoothing the image at a first smoothing level, generating a curvature image by applying a second derivative operator, locating second derivative local maxima in the curvature image that are orthogonal to a respective model point and within a search region having a first boundary on one side of the start model and a second boundary on an opposite side of the start model, generating a set of contours, shifting the start model to an outer boundary of the contours, and generating a segmentation mask of the object based on the shifted start model.

US Patent:

20130051676, Feb 28, 2013

Filed:

Apr 29, 2011

Appl. No.:

13/695347

Inventors:

Jeffrey C. Wehnes - Richardson TX, US
James P. Monaco - Piscataway NJ, US
David S. Harding - Austin TX, US
James H. Pike - Carrollton TX, US
Anbinh T. Ho - Dallas TX, US
Lawrence M. Hanafy - New Orleans LA, US

Assignee:

VUCOMP, INC. - Plano TX

International Classification:

G06K 9/46

US Classification:

382190

Abstract:

An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.

US Patent:

20130202165, Aug 8, 2013

Filed:

Apr 29, 2011

Appl. No.:

13/695357

Inventors:

Jeffrey C. Wehnes - Richardson TX, US
James H. Pike - Carrollton TX, US

Assignee:

VuCOMP ,Inc. - Plano TX

International Classification:

G06T 7/00

US Classification:

382128

Abstract:

An image analysis embodiment comprises subsampling a digital image by a subsample factor related to a first anomaly size scale, thereby generating a subsampled image, smoothing the subsampled image to generate a smoothed image, determining a minimum negative second derivative for each pixel in the smoothed image, determining each pixel having a convex down curvature based on a negative minimum negative second derivative value for the respective pixel, joining each eight-neighbor connected pixels having convex down curvature to identify each initial anomaly area, selecting the initial anomaly areas having strongest convex down curvatures based on a respective maximum negative second derivative for each of the initial anomaly areas, extracting one or more classification features for each selected anomaly area, and classifying the selected anomaly areas based on the extracted one or more classification features.

US Patent:

20170032535, Feb 2, 2017

Filed:

Apr 1, 2015

Appl. No.:

15/302846

Inventors:

- Nashua NH, US
- Tokyo, JP
Satoshi Kasai - Hachioji-shi, Tokyo, JP
Shinsuke Katsuhara - Koganei-shi, Tokyo, JP
James H. Pike - Carrollton TX, US
Muhammad F. Sabir - Allen TX, US
Jeffrey C. Wehnes - Richardson TX, US

Assignee:

iCAD, Inc. - Nashua NH
Konica Minolta, Inc. - Tokyo

International Classification:

G06T 7/00
G06K 9/46
G06K 9/34
G06T 3/00
G06K 9/32

Abstract:

Lung segmentation and bone suppression techniques are helpful pre-processing steps prior to radiographic analyses of the human thorax, as may occur during cancer screenings and other medical examinations. Autonomous lung segmentation may remove spurious boundary pixels from a radiographic image, as well as identify and refine lung boundaries. Thereafter, autonomous bone suppression may identify clavicle, posterior rib, and anterior rib bones using various image processing techniques, including warping and edge detection. The identified clavicle, posterior rib, and anterior rib bones may then be suppressed from the radiographic image to yield a segmented, bone suppressed radiographic image.

US Patent:

20160256126, Sep 8, 2016

Filed:

Nov 10, 2014

Appl. No.:

15/035743

Inventors:

Jeffery C. Wehnes - Richardson TX, US
Arunkumar Gururajan - Dallas TX, US
James Monaco - Frisco TX, US
Ronald Larcom - Frisco TX, US
James H. Pike - Carrollton TX, US

International Classification:

A61B 6/00
G06T 7/00

Abstract:

Breast density measurements are used to perform Breast Imaging Reporting and Data System (BI-RADS) classification during breast cancer screenings. The accuracy of breast density measurements can be improved by quantitatively processing digital mammographic images. For example, breast segmentation may be performed on a mammographic image to isolate the breast tissue from the background and pectoralis tissue, while a breast thickness adjustment may be performed to compensate for decreased tissue thickness near the skin line of the breast. In some instances, BI-RADS density categorization may consider the degree to which dense tissue is dispersed throughout the breast. A breast density dispersion parameter can also be obtained using quantitative techniques, thereby providing objective BI-RADS classifications that are less susceptible to human error.

US Patent:

20150023580, Jan 22, 2015

Filed:

Oct 6, 2014

Appl. No.:

14/507575

Inventors:

- Plano TX, US
James P. Monaco - Frisco TX, US
David S. Harding - Austin TX, US
James H. Pike - Carrollton TX, US
Anbinh T. Ho - Dallas TX, US
Lawrence M. Hanafy - New Orleans LA, US

International Classification:

G06T 7/00
G06T 5/10
G06T 5/00

US Classification:

382132

Abstract:

An analysis of a digitized image is provided. The digitized image is repeatedly convolved to form first convolved images, which first convolved images are convolved a second time to form second convolved images. Each first convolved image and the respective second convolved image representing a stage, and each stage represents a different scale or size of anomaly. As an example, the first convolution may utilize a Gaussian convolver, and the second convolution may utilize a Laplacian convolver, but other convolvers may be used. The second convolved image from a current stage and the first convolved image from a previous stage are used with a neighborhood median determined from the second convolved image from the current stage by a peak detector to detect peaks, or possible anomalies for that particular scale.