Abstract: An analysis system automatically analyzes and counts fluorescence signals present in biopsy tissue marked using Fluorescence in situ Hybridization (FISH). The user of the system specifies classes of a class network and process steps of a process hierarchy. Then pixel values in image slices of biopsy tissue are acquired in three dimensions. A computer-implemented network structure is generated by linking pixel values to objects of a data network according to the class network and process hierarchy. Objects associated with pixel values at different depths of the biopsy tissue are used to determine the number, volume and distance between cell components. In one application, fluorescence signals that mark Her2/neural genes and centromeres of chromosome seventeen are counted to diagnose breast cancer. Her2/neural genes that overlap one another or that are covered by centromeres can be accurately counted. Signal artifacts that do not mark genes can be identified by their excessive volume.

Abstract: An image-based biomarker is generated using image features obtained through object-oriented image analysis of medical images. The values of a first subset of image features are measured and weighted. The weighted values of the image features are summed to calculate the magnitude of a first image-based biomarker. The magnitude of the biomarker for each patient is correlated with a clinical endpoint, such as a survival time, that was observed for the patient whose medical images were analyzed. The correlation is displayed on a graphical user interface as a scatter plot. A second subset of image features is selected that belong to a second image-based biomarker such that the magnitudes of the second image-based biomarker for the patients better correlate with the clinical endpoints observed for those patients. The second biomarker can then be used to predict the clinical endpoint of other patients whose clinical endpoints have not yet been observed.

Abstract: In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.

Abstract: An analysis system analyzes digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers and object networks. Objects in a first object network are segmented into a first class, and objects in a second object network are segmented into a second class. One process step of the process hierarchy involves generating a third object network by imprinting objects of the first object network into the objects of the second object network such that pixel locations are unlinked from objects of the second object network to the extent that the pixel locations were also linked to objects of the first object network. The imprinting step allows object-oriented processing of digital images to be performed with fewer computations and less memory. Characteristics of an object of the third object network are then determined by measuring the object.

Abstract: A computer-implemented method for navigating between sections in a display space is furnished. The sections in the display space are each assigned to a particular location of the display space, and representatives are provided which are each assigned to a particular section in the display space using an unambiguous relation. The method for navigating includes the following steps: selecting at least one representative that is assigned to a particular section in the display space with the aid of the unambiguous relation; and navigating to the particular section in the display space with the aid of the unambiguous relation with the particular section in the display space.

Abstract: An analysis system analyzes and measures patterns present in the pixel values of digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers, thematic layers and object networks. Various types of processing are performed depending on whether the data of the digital images is represented in the image, thematic or object layers. Pixel-oriented and object-oriented processing is combined so that fewer computations and less memory are used to analyze the digital images. Pixel-oriented processes, such as filtering, are selectively performed only at pixel locations that are assigned to a specified thematic class of a thematic layer or that are linked to a particular object of the object network. Similarly, object-oriented processing is performed at pixel locations linked to objects generated using thematic layers or using image layers on which pixel-oriented processes have already been performed.

Abstract: A method for the use of fractal semantic networks is disclosed, wherein the fractal semantic network contains both semantic units that possess respective information contents as well as link units that describe a relation content that respectively links two semantic units such that the mutual relationship of the two linked semantic units is determined through the relation content. In that case, a knowledge network consists of category units and, as the case may be, additionally of instance units and/or Janus units. For the querying of information, classification and/or selecting of semantic sub-networks in this knowledge network, the networking of semantic units taking into consideration the type, content, composition and/or distance of other semantic units in the respective network environment can be employed. Furthermore, Janus functionality can be employed for the local classification or for the local alteration of the networking of a semantic unit.

Abstract: A method for processing data structures with the aid of networked semantic units includes: acquiring a data structure, and generating, modifying, deleting and storing semantic structure units and networking them on the basis of the acquired data structure while using a knowledge base comprised of a network of semantic knowledge units. Semantic structure units and their network are classified in iterative steps. Based on this classification, a specific processing is activated which modifies a respective semantic structure unit and a particular partial network.

Abstract: A computer-implemented network structure includes a semantic network machine comprised of nodes containing informational contents and links containing relational contents. Relational contents describe the relationship between linked nodes. Some of the nodes are semantic Janus units. Based on time-variable states of each semantic Janus unit, the semantic Janus units perform operations on nodes and links. The operations are focused on selected portions of the semantic network machine such that each semantic Janus unit need not deal with all possible informational and relational contents within the semantic network machine. The computational resources of the computer network are thereby efficiently managed, and artificial intelligence tasks such as inferential retrieval are performed quicker. The amount of data that is processed is substantially reduced by focusing on bundled information.

Abstract: What is disclosed is a computer-implemented method for finding objects which are based on data structures, the method comprising the following steps: announcing a selection of objects; generating and/or invalidating a characterization of one object or several objects of the selection, with characterization being effected by means of an item of characterization information; calculating weights of the objects of the selection and/or of objects located in a vicinity of the characterized objects based on the characterization information, weights already calculated, and/or weights already stored; and modifying the selection and continuing the method from the first step until objects to be found are contained in the selection, until the method is interrupted, or until a given or calculated number of method steps has been carried out, with the possibility of terminating or interrupting the method after any one of the preceding steps.

Abstract: A system for computer-aided detection uses a computer-implemented network structure to analyze patterns present in digital image slices of a human body and to generate a three-dimensional anatomical model of a patient. The anatomical model is generated by detecting easily identifiable organs first and then using those organs as context objects to detect other organs. A user specifies membership functions that define which objects of the network structure belong to the various classes of human organs specified in a class hierarchy. A membership function of a potentially matching class determines whether a candidate object of the network structure belongs to the potential class based on the relation between a property of the voxels linked to the candidate object and a property of the context object. Some voxel properties used to classify an object are location, brightness and volume. The human organs are then measured to assist in the patient's diagnosis.

Abstract: A method for processing data structures with the aid of networked semantic units. The method includes acquiring a data structure, and generating, modifying, deleting and/storing semantic structure units and/networking them on the basis of the acquired data structure. The method uses a knowledge base comprised of a network of semantic knowledge units. Semantic structure units and/their network are classified in iterative steps. Based on this classification, a specific processing is activated which modifies a respective semantic structure unit and/a particular partial network.

Abstract: A semiconductor substrate or wafer inspection device for detecting defects on wafer surfaces includes an air-cushion stage which can be displaced in two directions (X,Y) that are perpendicular to one another. Several air nozzles are provided for this purpose. At least one valve is connected to at least one electric control unit, the valve being configured in such a way that a normal pressure prevails in the air nozzles when the electric control unit delivers a corresponding signal.

Abstract: An approach for providing dynamic security access is presented. A dynamic security system receives a resource request from a user. The dynamic security system computes a dynamic user security value using a user security formula and user attribute values corresponding to the user. In addition, the dynamic security system computes a resource security value using a resource security formula and resource attribute values corresponding to the resource. Once computed, the dynamic security system compares the dynamic user security value with the resource security value and, if the dynamic user security value is greater than or equal to the resource security value, the dynamic security system grants resource access to the user.

Abstract: An analysis system automatically analyzes and counts fluorescence signals present in biopsy tissue marked using Fluorescence in situ Hybridization (FISH). The user of the system specifies classes of a class network and process steps of a process hierarchy. Then pixel values in image slices of biopsy tissue are acquired in three dimensions. A computer-implemented network structure is generated by linking pixel values to objects of a data network according to the class network and process hierarchy. Objects associated with pixel values at different depths of the biopsy tissue are used to determine the number, volume and distance between cell components. In one application, fluorescence signals that mark Her2/neural genes and centromeres of chromosome seventeen are counted to diagnose breast cancer. Her2/neural genes that overlap one another or that are covered by centromeres can be accurately counted. Signal artifacts that do not mark genes can be identified by their excessive volume.

Abstract: An analysis system analyzes and measures patterns present in the pixel values of digital images using a computer-implemented network structure that includes a process hierarchy, a class network and a data network. The data network includes image layers, thematic layers and object networks. Various types of processing are performed depending on whether the data of the digital images is represented in the image, thematic or object layers. Pixel-oriented and object-oriented processing is combined so that fewer computations and less memory are used to analyze the digital images. Pixel-oriented processes, such as filtering, are selectively performed only at pixel locations that are assigned to a specified thematic class of a thematic layer or that are linked to a particular object of the object network. Similarly, object-oriented processing is performed at pixel locations linked to objects generated using thematic layers or using image layers on which pixel-oriented processes have already been performed.

Abstract: The invention is based on an apparatus and a method for scanning specimens (1) using an optical imaging system (3) and a scanning stage (2), images of the specimen (1) being acquired by means of a camera (4), and/or measurements on the specimen (1) being made by means of an optical measurement device (5), at specimen points Xp, Yp. For that purpose, the scanning stage (2) is calibrated by obtaining and storing height values Z at different calibration positions X, Y of the scanning stage (2), and thereby generating a running height profile of the scanning stage (2). For the scanning of specimens (1), the specimen height positions Zp at specimen points Xp, Yp are determined by means of a reference height Zref of the specimen (1) together with the running height profile of the scanning stage (2).

Abstract: In a specification mode, a user specifies classes of a class network and process steps of a process hierarchy using a novel scripting language. The classes describe what the user expects to find in digital images. The process hierarchy describes how the digital images are to be analyzed. Each process step includes an algorithm and a domain that specifies the classes on which the algorithm is to operate. A Cognition Program acquires table data that includes pixel values of the digital images, as well as metadata relating to the digital images. In an execution mode, the Cognition Program generates a data network in which pixel values are linked to objects, and objects are categorized as belonging to classes. The process steps, classes and objects are linked to each other in a computer-implemented network structure in a manner that enables the Cognition Program to detect target objects in the digital images.

Abstract: The invention relates to a wafer inspection device. The device comprises an air-cushion stage which can be displaced in two directions (X,Y) that are perpendicular to one another. Several air nozzles are provided for this purpose. At least one valve is connected to at least one electric control unit, the valve being configured in such a way that a normal pressure prevails in the air nozzles when the electric control unit delivers a corresponding signal.

Abstract: A computer-implemented network structure includes a semantic network machine comprised of nodes containing informational contents and links containing relational contents. Relational contents describe the relationship between linked nodes. Some of the nodes are semantic Janus units. Based on time-variable states of each semantic Janus unit, the semantic Janus units perform operations on nodes and links. The operations are focused on selected portions of the semantic network machine such that each semantic Janus unit need not deal with all possible informational and relational contents within the semantic network machine. The computational resources of the computer network are thereby efficiently managed, and artificial intelligence tasks such as inferential retrieval are performed quicker. The amount of data that is processed is substantially reduced by focusing on bundled information.