James R Monaco, 42Los Angeles, CA

2011027, Nov 10, 2011

Jun 24, 2011

13/168614

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

VuCOMP, Inc. - Plano TX

G06K 9/34
G06K 9/00

382128, 382173

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.

2011028, Nov 17, 2011

Jun 24, 2011

13/168588

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

VuCOMP, Inc. - Plano TX

G06K 9/34

382132

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.

2013005, Feb 28, 2013

Apr 29, 2011

13/695347

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

VUCOMP, INC. - Plano TX

G06K 9/46

382190

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.

2016025, Sep 8, 2016

Nov 10, 2014

15/035743

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

A61B 6/00
G06T 7/00

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.

2015002, Jan 22, 2015

Oct 6, 2014

14/507575

- 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

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

382132

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.