Deus Technologies LLC

Riverain Medical Group, LLC

Xin-Wei Xu

Jyh-Shyan Lin

Ruiping Li

Yuan-Ming Fleming Lure

Hwa-Young Michael Yeh

Edward A Martello

Hui Zhao

Fleming Lure

Fleming Y-m Lure

Tong Wei

William Bredlow

Rong Feng Zhuang

Richard Lipscher

Matthew Thomas Freedman

Rujirutana Srikanchana

Rujirutana Sriikanchana

Rongfeng Zhang

H Michael Yeh

Li-Yueh Hsu

H-y Michael Yeh

Computer-aided diagnosis techniques may be combined with techniques to obtain derived images from radiographic images to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Computer aided diagnosis using derived images

Computer-aided diagnosis techniques may be combined with dual-energy radiography techniques to provide enhanced computer-aided diagnosis of, for example, lung nodules.

Computer aided diagnosis using dual energy subtraction images

A user interface for facilitating a computer-aided diagnosis (CAD) includes an image input device that receives and scans the multiple images, and a film feeder that transports films from a film developer to the scanner. A multi-image identification input device uses bar code reading, numeric keypad, keyboard, network, and/or mouse to enter multiple image identification prior to image scanning. A display includes an image identification area, case processing status area, and a CAD detection and user modification area. The user can edit CAD results&#x2014;accept, remove&#x2014;and can add new diagnosis results to create a new composite diagnosis result.

Input/output interface for computer aided diagnosis (CAD) system

A method of detecting and analyzing abnormalities, like lung nodules, in thoracic computer tomography (CT) images uses digital image processing techniques and adaptive computing methods. The techniques include an automatic detection process to detect candidate abnormalities, an image matching process to match CT slices from two different CT scans, and a measurement process that determines parameters of the candidate abnormalities. Final results and processed CT images are displayed on a user interface.

Computer-aided diagnosis system for thoracic computer tomography images

A fuzzy logic based classification (FLBC) method for the automated discrimination of objects and the automated identification of nodules based on their features, a computer programmed to implement the method, and a storage medium which stores a program for implementing the method, wherein nodule (or, object) features are first normalized and then automatically selected. Based on the selected features, suspect nodules (or, objects) are pre-grouped and then subjected to the corresponding trained linear classifier to remove those false positive nodules or abnormal objects that are linearly separable. Finally, the remaining suspect nodules or objects are further subjected to a trained fuzzy classifier for removing those false positive nodules or abnormal objects that are not linearly separable.

Fuzzy logic based classification (FLBC) method for automated identification of nodules in radiological images

A divide-and-conquer (DAC) method and system improve the detection of abnormalities, like lung nodules, in radiological images via the use of zone-based digital image processing and artificial neural networks. The DAC method and system divide the lung zone into different zones in order to enhance the efficiency in detection. Different image enhancement techniques are used for each different zone to enhance nodule images, as are different zone-specific techniques for selecting suspected abnormalities, extracting image features corresponding to selected abnormalities, and classifying the abnormalities as either true or false abnormalities.

Divide-and-conquer method and system for the detection of lung nodule in radiological images

X-ray film re-screening apparatus

Growing community of inventors

Gaithersburg, MD, United States of America

Xin-Wei Xu