Abstract: A computerized mask edit guided processing method for time-lapse image analysis performs by a computer program an assisted mask editing on an input image sequence to generate mask edit data, and performs a mask edit guided processing using the image sequence and the mask edit data. A computerized track edit guided processing method for time-lapse image analysis performs by a computer program an assisted track editing on an input image sequence to generate track edit data, and performs a track edit guided processing using the image sequence and the track edit data. A computerized edit guided processing method for time-lapse image analysis performs by a computer program a combination of assisted mask editing and assisted track editing on an input image sequence to generate edit data, and performs a combination of mask edit guided processing and track edit guided processing using the image sequence and the edit data.

Abstract: A computerized recipe station for time-lapse image analysis method includes the steps of inputting an image sequence and an initial recipe to a computer storage; performing by a computer program an incremental apply using the image sequence and the initial recipe to generate an incremental output; pausing the incremental apply; using the incremental output to perform an incremental output assurance operation, which may be an intermediate result analysis to generate an analysis output, a recipe update to generate an updated recipe, or a result editing to generate an edited incremental output; and continuing the incremental apply until pausing or completion to generate a processing output. The analysis output generated by the intermediate result analysis may be used to guide the recipe update step or used to guide the result editing step.

Abstract: Computerized image guided control method uses image quantification to dynamically monitor and control the cell generation process. The method acquires images during the cell generation steps and uses computer image analysis to automatically quantify the results of the cell generation steps. In one embodiment, the quantified results from the image analysis are used to determine the readiness of the cell generation step. In another embodiment, a remedial recovery sub-step for the cell generation step is also guided by the imaging results. In yet another embodiment, when a cell generation step is determined to have failed, the cell generation process is early terminated based on image guided decision.

Abstract: A computerized teachable pattern scoring method receives a teaching image and region pattern labels. A region segmentation is performed using the teaching image to generate regions of interest output. A feature measurement is performed using the teaching image and the regions of interest to generate region features output. A pattern score learning is performed using the region features and the region pattern labels to generate pattern score recipe output. A computerized region classification method using the region features and the pattern score recipe to generate pattern scores output. A region classification is performed using the pattern scores and region features to generate region class output.

Abstract: A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output. An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output.

Abstract: Computerized image guided control method uses image quantification to dynamically monitor and control the cell generation process. The method acquires images during the cell generation steps and uses computer image analysis to automatically quantify the results of the cell generation steps. In one embodiment, the quantified results from the image analysis are used to determine the readiness of the cell generation step. In another embodiment, a remedial recovery sub-step for the cell generation step is also guided by the imaging results. In yet another embodiment, when a cell generation step is determined to have failed, the cell generation process is early terminated based on image guided decision.

Abstract: A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output.

Abstract: A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output.

Abstract: A computerized robust cell kinetic recognition method for moving cell detection from temporal image sequence receives an image sequence containing a current image. A dynamic spatial-temporal reference generation is performed to generate dynamic reference image output. A reference based object segmentation is performed to generate initial object segmentation output. An object matching and detection refinement is performed to generate kinetic recognition results output. The dynamic spatial-temporal reference generation step performs frame look ahead and the reference images contain a reference intensity image and at least one reference variation image.

Abstract: A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. A partitioned modeling method receives an image region and performs feature extraction on the image region to generate characterization feature. A hierarchical partitioning is performed using the characterization feature to generate hierarchical partitions. A model generation is performed using the hierarchical partitions to generate partition model.

Abstract: A computerized spatial-temporal regulation method for accurate spatial-temporal model estimation receives a spatial temporal sequence containing object confidence mask. A spatial-temporal weight regulation is performed to generate weight sequence output. A weighted model estimation is performed using the spatial temporal sequence and the weight sequence to generate at least one model parameter output. An iterative weight update is performed to generate weight sequence output. A weighted model estimation is performed to generate estimation result output. A stopping criteria is checked and the next iteration iterative weight update and weighted model estimation is performed until the stopping criteria is met. A model estimation is performed to generate model parameter output. An outlier data identification is performed to generate outlier data output. A spatial-temporal data integrity check is performed and the outlier data is disqualified.

Abstract: A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. An update learning method performs pattern enhancement progressive update learning.

Abstract: A method for the detection and analysis of patterns receives an image containing object labels and performs relational feature development using the input image to create at least one pattern map. It then performs relational feature analysis using the at least one pattern map to create a relational feature analysis result. The pattern detection and analysis method further comprises a recipe for automation control and includes determination of a genetic anomaly. A relational feature development method receives an image containing object labels and performs core measurement table development using the input image to create at least one core measurement table. It then performs feature table production using the at least one core measurement table to create at least one feature table. It also performs PatternMap creation using the at least one feature table to create a PatternMap. The relational feature development method further comprises a PatternMap integration and update step to create an updated PatternMap.

Abstract: A Recognition Frame presents multi-level application elements to the user simultaneously through a computer graphical user interface. The interface consists of an image display panel for displaying image channels; a data display panel for displaying object measurements and summary statistics; a configuration display panel for displaying recipe content; a master tab for selecting the panels. It also consists of a processing toolbar for context dependent processing tool display. The Recognition Frame further comprises a second side frame for data object display and charting. The second side frame has a tabular arrangement consisting of properties tab, controls tab, and charts tab. The Recognition Frame links application elements through a complex data model wherein interface display is automatically updated when one element is changed.

Abstract: An adaptive dilation method receives an image and performs an adaptive background distance transform to create an adaptive background distance transform image. A threshold is applied to the adaptive background distance transform image to generate adaptive dilation image output. An adaptive erosion method receives an image and performs an adaptive foreground distance transform to create an adaptive foreground distance transform image. A threshold is applied to the adaptive foreground distance transform image to generate adaptive erosion image output.

Abstract: A partition pattern template generation method for alignment receives a learning image and performs partition template generation using the learning image to generate a plurality of partition template result output. A partition template acceptance test is performed using the plurality of partition template results to generate partition templates or failure result. A partition template search method for alignment receives an alignment image and partition templates and performs a plurality of template search steps to generate a plurality of matching scores output. A partition integration method is performed using the plurality of matching scores to generate a partition template search result. A partition integration error self checking method receives a preliminary template search result position and a plurality of the matching scores. A matching score profile comparison is performed using the plurality of the matching scores and the expected matching score profile to generate the template search result.

Abstract: An object analysis method performs object segmentation to generate segmentation confidence map and uses the segmentation results to generate robust object features. The robust object features are combined to create robust FOV summary features, robust sample summary features and robust assay summary features.

Abstract: A two pass Zone Of Influence (ZOI) creation method creates a Zone Of Influence (ZOI) image. The two pass ZOI creation method performs a first pass scan to create a first pass intermediate distance image and a shortest distance component label image. It then performs a second pass scan using the first pass intermediate distance image and the shortest distance component label image to create a background distance transform image and a updated shortest distance component label image. An adaptive image region partition method allows distance lengths of the two pass adaptive ZOI creation method depend on their associated component labels.

Abstract: An integrated human and computer interactive data mining method receives an input database. A learning, modeling, and analysis method uses the database to create an initial knowledge model. A query of the initial knowledge model is performed using a query request. The initial knowledge model is processed to create a knowledge presentation output for visualization. It further comprises a feedback and update request step that updates the initial knowledge model. A multiple level integrated human and computer interactive data mining method facilitates overview interactive data mining and dynamic learning and knowledge representation by using the initial knowledge model and the database to create and update a presentable knowledge model. It facilitates zoom and filter interactive data mining and dynamic learning and knowledge representation by using the presentable knowledge model and the database to create and update the presentable knowledge model.

Abstract: An object based boundary refinement method for object segmentation in digital images receives an image and a single initial object region of interest and performs refinement zone definition using the initial object regions of interest to generate refinement zones output. A directional edge enhancement is performed using the input image and the refinement zones to generate directional enhanced region of interest output. A radial detection is performed using the input image the refinement zones and the directional enhanced region of interest to generate radial detection mask output. In addition, a final shaping is performed using the radial detection mask having single object region output.