The invention provides methods for determining the probability that a given region of a tissue sample contains tissue of a given category, such as CIN 1 (cervical intraepithelial neoplasia, grade 1), CIN 2/3 (cervical intraepithelial neoplasia grades 2 and/or 3), normal squamous, normal columnar, and metaplasia, for example. The invention provides increased diagnostic accuracy by combining a plurality of statistical classification techniques. Furthermore, in one embodiment, the invention comprises combining one or more statistical techniques with one or more non-statistical classification techniques.
Methods and arrangement for providing digital x-ray mammography image acquisition by means of an X-ray system that includes acquiring image data by irradiating an object, such as a human breast, automatically by the system analyzing the acquired image data with respect to presence of motion blur, indicating whether motion blur is present.
A region-guided boundary refinement method for object segmentation in digital images receives an initial object regions of interest and performs directional boundary decomposition using the initial object regions of interest to generate a plurality of directional object boundaries output. A directional border search is performed using the plurality of directional object boundaries to generate base border points output. A base border integration is performed using the base border points to generate base borders output. In addition, a boundary completion is performed using the base borders having boundary refined object regions of interest output.A region-guided boundary completion method for object segmentation in digital images receives an initial object regions of interest and base borders. It performs boundary completion using the initial object regions of interest and the base borders to generate boundary refined object regions of interest output. The boundary completion method performs border growing using the base borders to generate grown borders output. A region guided connection is performed using the grown borders to generate connected borders output. A region guided merging is performed using the connected borders to generate fined object regions of interest output.
An active learning system samples known data, and learns the known data independently in a plurality of learning machines, and selects data to be next learned for unknown data. The active learning system comprises a sampling weighting device for weighting the known data when they are sampled, a prediction weighting device for weighting the learning results of the learning machines when they are integrated, and a data weighting device for weighting the learning results when the data to be next learned is selected. When an extreme deviation is present in the count of data, each of the weighting devices more heavily weights a larger count of data.
Embodiments of the present invention are directed to quantitative analysis of tissues enabling the measurement of objects and parameters of objects found in images of tissues including perimeter, area, and other metrics of such objects. Measurement results may be input into a relational database where they can be statistically analyzed and compared across studies. The measurement results may be used to create a pathological tissue map of a tissue image, to allow a pathologist to determine a pathological condition of the imaged tissue more quickly.
