Charles Schwab Co., Inc.

Vucomp, Inc.

Konica Minolta, Inc.

Icad, Inc.

David S Harding

Bradley J Swearingen

Richard Munoz, Jr

Brant C Lewis

Gregory Scott Mogonye

Jeffrey C Wehnes

Gregory Ferris

Philip R Berber

James H Pike

James P Monaco

Anbinh T Ho

Lawrence M Hanafy

Shinsuke Katsuhara

Satoshi Kasai

Muhammad Sabir

Sridharan Kamalakanan

Muhammad F Sabir

Lung segmentation and bone suppression techniques are helpful pre-processing steps prior to radiographic analyzes 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.

Lung segmentation and bone suppression techniques for radiographic images

A computerized system () and method for integrating trade executions among multiple market participant types is disclosed. The system () includes a client system () that provides a user interface () for inputting an order for the execution of a trade and a server system () in communication with the client system (). The server system () accepts the order for the execution of the trade from the client system (). The server systems () then routes the order to a trade execution location () of one market participant type selected from among multiple market participant types such as the market participant types of ECNs, market makers and exchanges. The server system () has direct and indirect access to the ECNs and the market makers and has indirect access to the exchanges, thereby integrating trade executions among multiple market participant types.

Method and system for integrating trade executions among multiple market participant types

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.

Microcalcification detection and classification in radiographic images

An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.

Spiculated malignant mass detection and classification in a radiographic image

An image analysis embodiment comprises generating a bulge mask from a digital image, the bulge mask comprising potential convergence hubs for spiculated anomalies, detecting ridges in the digital image to generate a detected ridges map, projecting the detected ridges map onto a set of direction maps having different directional vectors to generate a set of ridge direction) projection maps, determining wedge features for the potential convergence hubs from the set of ridge direction projection maps, selecting ridge convergence hubs from the potential convergence hubs having strongest wedge features, extracting classification features for each of the selected ridge convergence hubs, and classifying the selected ridge convergence hubs based on the extracted classification features.

Spiculated malignant mass detection and classification in radiographic image

Microcalcification detection classification in radiographic images

A system for graphically differentiating user preferred securities from one another is disclosed. The system comprises a server system () and a client system (). The server system () is in communication with a security data source () that provides security data on a plurality of securities. The client system () is in communication with the server system () and provides M user specific criteria to the server system (). The server system () then analyzes the security data based upon the M user specific criteria and identifies user preferred securities from the plurality of securities. The server system () provides the client system () with data relating to the user preferred securities. The client system () uses N user specific parameters received from an input device () to generate an N dimensional graph that is populated with icons representing the user preferred securities, thereby graphically differentiating the user preferred securities from one another on a display device ().

Method and system for graphically differentiating user preferred securities from one another

A computerized system () and method for obtaining the best fill for an order using automated suborders is disclosed. The system () includes a client system () that proves a user interface () for inputting an order and a single click operation for initiating the order. The system () also includes a server system () that is in communication with the client system () and a plurality of trade execution locations (). When the server system () receives the order from the client system (), the server system () parses the order into a plurality of suborders. The server system () then sends each of the suborders to one of the market participants which are selected and sorted based upon having the best available price for a particular suborder. As such, if all of the suborders are filled, the best price is obtained for the order.

Method and system for obtaining the best fill for an order using automated suborders

Disclosed are methods and systems of executing securities trades. An exemplary system comprises a server system that is in communication with a client system, a security data source, and a trade execution location. The security data source provides security data relating to a plurality of securities to the server system. The client system provides user specific criteria to the server system. The server system then analyzes the security data based upon the criteria, identifies user preferred securities from the plurality of securities, and provides the client system with data relating to the user preferred securities. The client system uses N user specific parameters to generate and display an N dimensional graph that is populated with icons representing the user preferred securities. After a user preferred securities is selected from the graph and an order is requested, the client system sends the order to the server system.

Method and system for executing trades in a user preferred security

A system for executing trades in a user preferred security comprising a server system () in communication with a client system (), a security data source () and a trade execution location (). The security data source () provides security data relating to a plurality of securities to the server system (). The server system () analyzes the security data based upon M user specific criteria, and identifies user preferred securities from the plurality of securities and provides the client system () with data relating to the user preferred securities. The client system () uses N user specific parameters to generate and display an N dimensional graph that is populated with icons representing the user preferred securities. After a user preferred security is selected, the server system () routes the order to a trade execution location ().

Growing community of inventors

Austin, TX, United States of America

David S Harding