Abstract: A method is disclosed for use in eye registrations and examinations, some embodiments of the method including acquiring scan pattern scans; forming images from the scans; registering the images; generating a feature map from the scan pattern scans; identifying features on the feature map; and recording positional information of features.

Abstract: A method is disclosed for use in eye registrations and examinations, some embodiments of the method including acquiring scan pattern scans; forming images from the scans; registering the images; generating a feature map from the scan pattern scans; identifying features on the feature map; and recording positional information of features.

Abstract: An automated method and system for determining the positional orientation of radiographic images, wherein digital image data is acquired and subjected to multiple phases of digital imaging processing. The image is processed to segment the object of interest, i.e., a body region, from the foreground and background of the image. Next, the center is determined for the body region. The body region center is then positioned so that it coincides with the center of the film or medium. The image is subsequently decomposed into one or more horizontal and vertical strips. Feature extraction is performed on these strips to preliminarily determine the orientation of the image. Finally, a set of rules is applied to the image information described above to determine the positional orientation of the image.

Abstract: An image segmentation method for segmenting various regions of digital radiographic images first uses progressively smoothing techniques to generate smoothed images at multiple scales (resolutions). Next, texture measures are computed at each scale for each pixel in the smoothed image. Then a clustering algorithm is applied to the feature space spanned by the vector of texture measures. A segment spectrum, which is the number of clusters as a function of scale, is subsequently constructed to determine the most stable range of scales and the most stable number of regions into which the image is best segmented. Each pixel in the image is then classified into one of the detected regions according to its belonged cluster in the feature space. Region filling is then performed to eliminate small holes in each segmented region. Finally, a segmented map is generated to function as multivalued templates for any further image processing to be done on various segmented regions of the image.