Abstract: A computer-aided method for detecting, classifying, and displaying candidate abnormalities, such as microcalcifications and interstitial lung disease in digitized medical images, such as mammograms and chest radiographs, a computer programmed to implement the method, and a data structure for storing required parameters, wherein in the classifying method candidate abnormalities in a digitized medical image are located, regions are generated around one or more of the located candidate abnormalities, features are extracted from at least one of the located candidate abnormalities within the region and from the region itself, the extracted features are applied to a classification technique, such as an artificial neural network (ANN) to produce a classification result (i.e., probability of malignancy in the form of a number and a bar graph), and the classification result is displayed along with the digitized medical image annotated with the region and the candidate abnormalities within the region.

Abstract: A computerized imaging system (such as CAT scan, MRI imaging, ultrasound imaging, infrared, X-ray, UV/visible light fluorescence, Raman spectroscopy or microwave imaging) is employed to sense the position of an endoscopic treatment system within the body of a patient. In a preferred embodiment, the system provides real-time computer control to maintain and adjust the position of the treatment system and/or the position of the patient relative to the treatment system; and also provides (if desired) real-time computer control of the operation of the treatment system itself. Other embodiments include scanning mechanisms for directing laser light or other radiation under controlled conditions at select locations within the body.

Abstract: The subject invention provides a method for diagnosing prostate cancer and determining preoperatively the pathological stage in patients with prostate cancer. The methods described herein can be used for prediction of margin positivity, seminal vesicle (S.V.) involvement, and lymph nodal (L.N.) involvement in patients with clinically localized prostate cancer. The method includes use of a neural network which provides prostate cancer stage information for a patient based upon input data which includes the patient's preoperative serum PSA, biopsy Gleason score, and systemic biopsy-based information. Its positive predictive value (PPV), negative predictive value (NPV), and accuracy are superior to that of current nomograms in use. Use of this method can result in enormous cost savings by accurately diagnosing patients with prostate cancer and by avoiding multiple imaging tests and expensive surgery in unindicated patients.

Abstract: An apparatus for detecting Deep Vein Thrombosis (DVT) in a patient includes a computer based device, a device disposed on a predetermined position on a calf of the patient for measuring blood volume, another device for measuring temperature of the calf and still another device for measuring calf size. A cuff is operably connected to the computer based device and envelops a portion of a thigh of the patient and is controllably restrictable by the computer based device to produce a controlled venous occlusion of the patient's deep veins for a predetermined period. A method for detecting DVT using the device is also provided.

Abstract: Delays in event detection in time-varying data can be reduced by predicting the time-varying data and then detecting the event in the predicted data. This finds application in the triggering of medical imaging devices, where physiological events can be detected in the time-varying data. An artificial neural network can be trained to predict data such as ECG signals from which a detection algorithm can accurately predict the occurrence of an event that will serve as a reference point for triggering.

Abstract: An automated computer-aided diagnosis (CAD) method and system using artificial neural networks (ANNs) for the quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized two-dimensional chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs. The second ANN was trained using vertical output patterns obtained from the 1.sup.st ANN for each ROI. The output value of the 2.sup.nd ANN was used to distinguish between normal and abnormal ROIS with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a certain threshold level, the chest image was considered abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images where the chest image was not clearly normal or abnormal.

Abstract: The present invention, in one form, corrects any error due to varying z-axis detector cell gains represented in data obtained by a scan in a CT system. In accordance with one form of the present invention, and after correcting the image data for beam-hardening, the data is passed through a highpass filter to remove any data representing relatively slow, or low frequency, changes. Next, the filtered data is clipped and view averaged to remove high frequency data contents due to the objects being imaged. A slope estimate is then created. Using the slope estimate, an error estimate is generated. The error estimate is then subtracted from the beam-hardened corrected data, for example. As a result, errors due to z-axis gain variation of the detector cells are removed from the projection data array.