Abstract: A clinical decision support (CDS) system comprises: a case grouping sub-system (10) including a graphical user interface (30) that is operative to simultaneously display data representing a plurality of patient cases and further configured to enable a user to group selected patient cases represented by the simultaneously displayed data into clinically related groups (32) as selected by the user; a probative features determination sub-system (12) that is operative to determine probative features (44) that correlate with the clinically related groups; and a CDS user interface (16) that is operative to receive current patient data relating to a current patient case and to output clinical decision support information based on values of the probative features determined from the received current patient data.

Abstract: A classifier (20) is trained by a feature matrix (18, 18?) made up of feature vectors (F11, . . . , Fkm). The feature vectors are generated by operating on each of a plurality (k) of training image data sets with each of a plurality (m) of image processing algorithms (121, . . . , 12m) to generate processed and segmented images. Features of the segmented regions are extracted (14) to generate the feature vectors. In this manner, the classifier is trained with data generated with a variety of image processing algorithms.

Abstract: A system and method of providing computer-aided analysis of medical images uses an image processor (910) to process medical image data. A decision engine (920) generates a diagnosis based on the image data (940). The decision engine estimates the probability of an illness based on the image data and assesses the relevance of any unavailable data. The result is used to request this unavailable data from the user for computing a more complete diagnosis or otherwise displaying the results in incomplete form due to either the lack of additional data or the confidence in the incomplete diagnostic results. The diagnostic results may be displayed on an output terminal (970) and may be stored in the database (930).

Abstract: Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Abstract: When treating a patient, clinical decision support system (CDSS) guidelines are employed to assist a physician in generating a treatment plan. These plans are generated using both imaging and non-imaging data. To accomplish this, the CDSS is interfaced with imaging systems (CADx, CAD, PACS etc.). A data-mining operation is performed to identify relevant patients with similar attributes such as diagnosis, medical history, treatment, etc from imaging and non-imaging data. Natural language processing is employed to extract and encode relevant non-imaging (textual) data from relevant patients' records. Additionally, an image of a current patient is compared to reference images in a patient database to identify relevant patients. Relevant patients are then identified to a user, and the user selects a relevant patient to view detailed information related to medical history, treatment, guidelines, efficacy, and the like.

Abstract: The invention relates to search for cases in a database. According to the proposed method and apparatus, similarity matching is performed between an input case and a set of cases in an initial search to receive similar cases by—using a given matching criterion. Then statistics on image and non-image-based features associated with the similar cases are calculated and presented to the user with the similar cases. In a search refinement the similar cases are refined by additional features that are determined by the user based on the statistics. The search refinement can be iterative depending on the user's need.

Abstract: A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data.

Abstract: This invention relates to a method and device for case-based decision support. It proposes that a case-based decision support system is trained on inputs from several radiologists in order to have a “baseline” system, and then the system provides an option to a radiologist to refine the baseline system based on his/her inputs which either refine weights of features for similarity distance computation directly or provide new similarity ground truth clusters. By enabling modifying the similarity distance computation based on user inputs, this invention adapts similarity ground truth to different users with different experience and/or different opinions.

Abstract: A system and method for cross-modality case-based computer-aided diagnosis comprises storing a plurality of cases, each case including at least one image of one of a plurality of modalities and non-image information, mapping a feature relationship between a feature from images of a first modality to a feature from images of a second modality, and storing the relationship.

Abstract: Methods and apparatus for training a system for developing a process of data mining, false positive reduction, computer-aided detection, computer-aided diagnosis and artificial intelligence are provided. A method includes choosing a training set from a set of training cases using systematic data scaling and creating a classifier based on the training set using a classification method. The classifier yields fewer false positives. The method is suitable for use with a variety of data mining techniques including support vector machines, neural networks and decision trees.

Abstract: Methods for performing genetic algorithm-based feature selection are provided herein. In certain embodiments, the methods include steps of applying multiple data splitting patterns to a learning data set to build multiple classifiers to obtain at least one classification result; integrating the at least one classification result from the multiple classifiers to obtain an integrated accuracy result; and outputting the integrated accuracy result to a genetic algorithm as a fitness value for a candidate feature subset, in which genetic algorithm-based feature selection is performed.

Abstract: A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data. The method includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, wherein a data stratification method is used to balance the number of cases in different classes. The subset determined by GA is used to train the support vector machine to classify candidate region/volumes found within non-training data.

Abstract: A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-processing machine learning to maximize specificity and sensitivity of the classification to realize a reduction in number of false positive detections reported. The method includes training a classifier on a set of medical image training data selected to include a number of true and false regions, wherein the true and false regions are identified by a CAD process, and automatically segmented, wherein the segmented training regions are reviewed by at least one specialist to classify each training region for its ground truth, i.e.

Abstract: A method for computer aided detection (CAD) and classification of regions of interest detected within HRCT medical image data includes post-CAD machine learning techniques applied to maximize specificity and sensitivity of identification of a region/volume as being a nodule or non-nodule. The regions are identified by a CAD process, and automatically segmented. A feature pool is identified and extracted from each segmented region, and processed by genetic algorithm to identify an optimal feature subset, which subset is used to train the support vector machine to classify candidate region/volumes found within non-training data.

Abstract: Optimizing example-based computer-aided diagnosis (CADx) is accomplished by clustering volumes-of-interest (VOIs) (116) in a database (120) into respective clusters according to subjective assessment of similarity (S220). An optimal set of volume-of-interest (VOI) features is then selected for fetching examples such that objective assessment of similarity, based on the selected features, clusters, in a feature space, the database VOIs so as to conform to the subjectively-based clustering (S230). The fetched examples are displayed alongside the VOI to be diagnosed for comparison by the clinician. Preferably, the displayed example is user-selectable for further display of prognosis, therapy information, follow up information, current status, and/or clinical information retrieved from an electronic medical record (S260).

Abstract: A system and method for generating high frame rate motion compensated color sequencing data for a color sequential display system. A high frame rate motion compensation color sequencing system (10) is provided, comprising: a system for receiving a first input frame and a second input frame, and for receiving motion vectors (18) associated with the input frames (16); and an interpolation system (12) that processes the motion vectors and input frames and generates high frame rate motion compensated color sequence data (20) with an output frame rate defined by an upconversion factor (26).

Abstract: A system and method for compressing a mixed graphic and video signal. A system is provided that comprises: an encoder (14) for compressing mixed graphic and video pixel blocks (56), including: a classification system (22) for classifying each inputted pixel block as one of a plurality of unique types of blocks; a plurality of encoder subsystems (32,34,36,38), wherein each of the encoder subsystems is configured to compress a unique type of block; and a rate control system (54) for attaining a target compression rate for a stream of compressed blocks; and a decoder (18) for decoding compressed pixel blocks received over an embedded communication channel from the encoder, wherein the decoder includes a plurality of decoder subsystems (62,64,66,68), each configured to uncompress a unique type of compressed block.

Abstract: A system and method of processing a digital video signal for display on a display device, includes decoding an encoded digital video signal to produce a decoded digital video signal having a video source format; extracting coding information from the encoded digital video signal; executing a video quality improvement algorithm on the decoded digital video signal having the video source format using the extracted coding information, to produce a processed decoded digital video signal having the video source format; and converting the processed decoded digital video signal from the video source format to a video display format suitable for display on the display device. The system and method enhance the quality and/or reduce video artifacts in a video signal after it is decoded and prior to display on a display device.

Abstract: A method of improving luminance transition includes decoding a coded video bitstream around a transition from a first luminance level to a second luminance level; and providing luminance transition enhancement based on a metric indicative of video bitstream. Additionally, an apparatus that improves a luminance transition includes a video decoder and a module that determines a metric indicative of the degree of video coding artifacts in a signal. The apparatus also includes a video processing module that includes a luminance transient enhancement module. The luminance enhancement module provides transition based on at least a value of the metric at the transition.

Abstract: A method of determining processing a coded video signal includes decoding the coded video signal; determining a global indicator value for a frame from the decoded video; and providing video processing to the decoded video of the frame based on the global indicator. Additionally, an apparatus for processing coded digital video signals includes a coded video decoder; a metric calculation module; and a video processing module, wherein the metric calculation module calculates at least one value of a global indicator, which indicates the quality of the coded video signal, and where the metric calculation module provides the global indicator to the video processing module, which selectively addresses the coded video signal depending on the value.