Deus Technologies LLC

Riverain Medical Group, LLC

Ge Medical Systems Global Technology Company, LLC

Caelum Research Corporation

Jyh-Shyan Lin

Xin-Wei Xu

Ruiping Li

Hwa-Young Michael Yeh

Yuan-Ming Fleming Lure

Edward A Martello

Hui Zhao

Fleming Lure

Fleming Y-m Lure

Tong Wei

Stephen Wayne Metz

Carson Hale Thomas

William Bredlow

H-y Michael Yeh

Fleming Y M Lure

Rong Feng Zhuang

Yuan-Ming F Lure

Richard Lipscher

Rujirutana Srikanchana

Matthew Thomas Freedman

Rongfeng Zhang

H Michael Yeh

Rujirutana Sriikanchana

Li-Yueh Hsu

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

A method is disclosed, in a diagnostic medical environment, that enhances workflow while using a computer-aided-detection (or diagnosis) (CAD) system in the environment. The method comprises generating image data with a diagnostic medical imaging acquisition system. The image data is transmitted from the diagnostic medical imaging acquisition system to a computer-aided-detection (or diagnosis) (CAD) system and an archive/review station. Detection results are generated by processing the image data, using the CAD system, while storing and viewing the image data on the archive/review station. The detection results are transmitted from the CAD system to the archive/review station. The detection results are integrated with the image data by the archive/review station to form composite image data. The composite image data is displayed by the archive/review system.

Workflow for computer aided detection

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 method and system improve the detection of abnormalities, such as lung nodules, in radiological images using digital image processing and artificial neural network techniques. The detection method and system use a nodule phantom for matching in order to enhance the efficiency in detection. The detection method and system use spherical parameters to characterize true nodules, thus enabling detection of the nodules in the mediastinum. The detection method and system use a multi-layer back-propagation neural network architecture not only for the classification of lung nodules but also for the integration of detection results from different classifiers. In addition, this method and system improve the detection efficiency by recommending the ranking of true nodules and several false positive nodules prior to the training of the neural network classifier. The method and system use image segmentation to remove regions outside the chest in order to reduce the false positives outside the chest region.

Method and system for the detection of lung nodule in radiological images using digital image processing and artificial neural network

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

An automated detection method and system improve the diagnostic procedures of radiological images containing abnormalities, such as lung cancer nodules. The detection method and system use a multi-resolution approach to enable the efficient detection of nodules of different sizes, and to further enable the use of a single nodule phantom for correlation and matching in order to detect all or most nodule sizes. The detection method and system use spherical parameters to characterize the nodules, thus enabling a more accurate detection of non-conspicuous nodules. A robust pixel threshold generation technique is applied in order to increase the sensitivity of the system. In addition, the detection method and system increase the sensitivity of true nodule detection by analyzing only the negative cases, and by recommending further re-assessment only of cases determined by the detection method and system to be positive. The detection method and system use multiple classifiers including back propagation neural network, data fusion, decision based pruned neural network, and convolution neural network architecture to generate the classification score for the classification of lung nodules. Such multiple neural network architectures enable the learning of subtle characteristics of nodules to differentiate the nodules from the corresponding anatomic background. A final decision making then selects a portion of films with highly suspicious nodules for further reviewing.

Method and system for re-screening nodules in radiological images using

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North Potomac, MD, United States of America

Jyh-Shyan Lin