Johns Hopkins University

William C Walton

Christopher M Gifford

Cash J Costello

Marc A Kolodner

Mary R Keller

Benjamin M W Tsui

David W Porter

Susan C Harvey

Donald A Ruffle

Keith S Peyton

Elissa M Weidaw

Seung-Jun Kim

Nathaniel S Winstead

A method of identifying potential lesions in mammographic images may include operations executed by an image processing device including receiving first image data of a first type, receiving second image data of a second type, registering the first image data and the second image data by employing a CNN using pixel level registration or object level registration, determining whether a candidate detection of a lesion exists in both the first image data and the second image data based on the registering of the first image data and the second image data, and generating display output identifying the lesion.

Method and apparatus for registration of different mammography image views

A method for characterizing and locating abnormal tissue in an area of interest in a body is provided. The method may include receiving first and second modality examination data including a plurality of first and second modality examination data segments of the area of interest and determining, by evaluating each first and second modality examination data segment, a set of modality detection candidates based on the first and second modality examination data and a first and second modality feature likelihood model. The method may also include generating a modal or multi-modal correspondence anatomic model of the area of interest by registering the plurality of first and second modality examination data segments, determining one or more modal abnormal tissue detections by fusing the set of modality detection candidates using the modal or multimodal correspondence anatomic model, and providing a modal diagnosis confidence score and a modal anatomic location based on the one or more modal abnormal tissue detections.

Abnormal tissue detection via modal upstream data fusion

An ice analyzer includes processing circuitry configured to receive a radiometer image including a geographic area including ice, receive a radar image including at least a portion of the geographic area, perform ice/water discrimination of the radiometer image and the radar image, generate a passive ice/water mask and an active ice/water mask based on the ice/water discrimination, merge the passive ice/water mask and the active ice/water mask into a typing mask, and type the ice based on the typing mask.

Ice analysis based on active and passive radar images

A detection system includes processing circuitry configured to receive overhead image data divided into a plurality of image chips and receive metadata associated with the image data. The metadata includes ground sample distance information associated with the image data and provides an indication of ground area represented by each pixel within the image chips. The processing circuitry is further configured to screen the image chips for candidate detections based on a multi-stage screening process and determine whether to classify candidate detections as target detections. The process includes an intensity based screening stage, an object extraction stage that employs binary shape features to extract objects from detect positions identified based on an output of the intensity based screening stage, and a candidate detection identification stage employing template based and structural feature criteria to identify candidate detections from an output of the object extraction stage.

Tactical object finder

A system and method of automatically creating a NIMA certified line drawing from a National Imagery Transmission Format (NITF) image file. A grayscale (electro-optical) image and its associated metadata from a NITF file is input into the computer based system. Pre-screening operations are performed on the metadata prior to image processing to ensure that National Imagery and Mapping Agency (NIMA) requirements and criteria are met. Image processing algorithms then perform filtering, encoding, edge detection, and custom edge pattern classification procedures to create a declassified line drawing. The line drawing can then be saved in any number of standard commercial file formats.

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Severn, MD, United States of America

William C Walton