Automation of microscopic pathological diagnosis relies on digital image quality, which, in turn, affects the rates of false positive and negative cellular objects designated as abnormalities. Cytogenetic biodosimetry is a genotoxic assay that detects dicentric chromosomes (DCs) arising from exposure to ionizing radiation. The frequency of DCs is related to radiation dose received, so the inferred radiation dose depends on the accuracy of DC detection. To improve this accuracy, image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When sufficient numbers of high quality images are found, the microscope system is directed to terminate metaphase image collection for a sample. The International Atomic Energy Agency recommends at least 500 images be used to estimate radiation dose, however often many more images are collected in order to select the metaphase cells with good morphology for analysis. Improvements in DC recognition increase the accuracy of dose estimates, by reducing false positive (FP) DC detection. A set of chromosome morphology segmentation methods selectively filtered out false DCs, arising primarily from extended prometaphase chromosomes, sister chromatid separation and chromosome fragmentation. This reduced FPs by 55% and was highly specific to the abnormal structures (≥97.7%). Additional procedures were then developed to fully automate image review, resulting in 6 image-level filters that, when combined, selectively remove images with consistently unparsable or incorrectly segmented chromosome morphologies. Overall, these filters can eliminate half of the FPs detected by manual image review. Optimal image selection and FP DCs are minimized by combining multiple feature based segmentation filters and a novel image sorting procedure based on the known distribution of chromosome lengths. Consequently, the average dose estimation error was reduced from 0.4 Gy to <0.2 Gy with minimal manual review required. Automated image selection with these filters reduces the number of images that are required to capture metaphase cells, thus decreasing the number of images and time required for each sample. A microscope system integrates image selection procedures controls with an automated digitally controlled microscope then determines at what point a sufficient number of metaphase cell images have been acquired to accurately determine radiation dose, which then terminates data collection by the microscope. These image filtering approaches constitute a reliable and scalable solution that results in more accurate and rapid radiation dose es

A similar case image search method performed by a computer, the method includes: extracting a lung field area from a medical image and identifying a contour of the lung field area including a chest wall and a mediastinum; identifying a position at which the chest wall and the mediastinum are internally divided and dividing the lung field area into a central area and a peripheral area based on a shape of the lung field area; counting the number of pixels indicating lesions in each of the divided central area and peripheral area; and identifying a similar case image corresponding to similarity level of the number of pixels indicating lesions by referring to a storage unit that stores the number of pixels indicating lesions in each of the areas.

The invention concerns a method implemented by a device for displaying strokes of digital ink in a display area and for performing text line extraction to extract text lines from the strokes. In particular, the text line extraction may involve slicing the display area into strips, ordering for each strip the strokes into ordered lists which form collectively a first set of ordered lists, forming for each strip a second set of ordered lists by filtering out from the ordered lists of the first set strokes which are below a given size threshold, and performing a neural net analysis based on said first and second sets to determine for each stroke a respective text line to which it belongs.

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

A boundary map and a first image are received. The boundary map is used to determine that brightness values for a set of pixels included in the first image should be regularized. The first set of pixels include, at a first pixel position, a first pixel having a first set of brightness values. The first set of pixels further includes, at a second pixel position, a second set of brightness values. An output image is generated by storing, at both a first and second pixel position in the output image, a set of regularized values.

A method for locating a three-dimensional target with respect to a vehicle is disclosed including capturing an image of the target, and from a three-dimensional mesh of the target, and from an estimation of the pose of the target, determining a set of projecting edges of the mesh of the target in the pose. The step of determining the projecting edges of the mesh of the target includes positioning the mesh of the target according to the pose, projecting in two dimensions the mesh so positioned, scanning the projection of the mesh in a plurality of scanning rows and, for each scanning row: defining a set of segments, each segment corresponding to the intersection of a face of the mesh with the scanning row and being defined by its ends, analyzing the relative depths of the ends of the segments, the depth being the position along a third dimension orthogonal to the two dimensions of the projection, in order to select a set of end points of segments corresponding to projecting edges of the mesh.

A method for generating a highly distinctive signature of a certain diamond, the method may include generating, based on one or more images of the certain diamond, a certain diamond signature of the certain diamond; finding, out of a group of reference diamonds, other diamonds having other diamond signatures; wherein the finding comprises calculating similarities between the certain diamond signature and reference diamond signatures of the reference diamonds of the group; and generating a new certain diamond signature that significantly differs from signatures of the other diamonds.

A gesture recognition method and a gesture recognition device are provided. The gesture recognition method includes the steps of: obtaining a hand image including a gesture graphic; determining a reference point in the gesture graphic; determining circular arc reference lines by using the reference point as a center; determining intersection points of each of the circular arc reference lines intersecting with a boundary of the gesture graphic; determining whether at least two finger blocks of a plurality of finger blocks of the gesture graphic conform to an approaching trend according to the circular arc reference lines and the intersection points, and determining whether the at least two finger blocks in a selected range of the gesture graphic forms a continuous graphic block; and when the at least two finger blocks of the gesture graphic conform to the approaching trend and form the continuous graphic block, determining the hand image as a hand pinch image.

A shape discriminating device includes an image acquisition means configured to acquire a target image being an image where pants are shown, a diverging position acquisition means configured to acquire, as a diverging position, a position where the pants diverge into two leg parts or a position which can be regarded as a position where the pants diverge into two leg parts, a leg region extraction means configured to extract a leg region from a region showing the pants in the target image, a leg shape acquisition means configured to acquire leg shape information concerning variation in lateral width of a leg part of the pants based on the diverging position and the leg region, a specifying means configured to specify a shape of the pants based on the leg shape information, and an output means configured to output information concerning the specified shape of the pants.

Systems and methods described herein relate to detecting objects. One embodiment receives a plurality of three-dimensional (3D) data points from a plurality of light beams emitted by one or more sensors; identifies, among the plurality of 3D data points, a first set of inlier points that satisfy a first predetermined error condition with respect to a plane hypothesis and a first set of outlier points that fail to satisfy the first predetermined error condition; identifying, among the first set of inlier points, a second set of outlier points, the second set of outlier points failing to satisfy a second predetermined error condition in a range domain with respect to a plurality of line hypotheses corresponding, respectively, to the plurality of light beams; and detecting an object based, at least in part, on at least one of the first set of outlier points and the second set of outlier points.