Abstract: The present invention extends to wagons with a bathroom module and standalone bathroom modules. A bathroom module includes a water heater connected to a sink, a shower, and a toilet. The bathroom module further includes a protective layer on the exterior and an exhaust fan to facilitate the management of moisture that may build up within the bathroom module. The bathroom module can be connected to various systems provided by a wagon, such as water, gas, electrical, A/C, heat, and sewer systems connected to external sources. Alternatively, the bathroom module can standalone and include its own water, gas, electrical, A/C, heat, and sewer systems connected to external sources. Thus, when paired with a companion wagon, the bathroom module may be installed or removed as desired.

Abstract: A method of preplanning orthopedic surgery and computer program therefore. The method includes the steps of: importing an x-ray image into an electronic canvas; scaling the x-ray image to determine the pixel to distance ratio; creating on the canvas one or more moveable, rotatable trace overlays of bones or bone fragments visible in the x-ray; inserting onto the canvas one or more moveable, rotatable images of orthopedic implants; and rotating and moving the trace overlays and the images of orthopedic implants into positions depicting the planned surgical outcome.

Abstract: A technique for segmenting a ribcage, e.g., in a chest radiograph, may involve determining whether or not the top of the image cuts through the lungs. The process may then proceed to segment the top and the sides of the ribcage. A dynamic programming technique may be used to perform the segmentation.

Abstract: A method of obtaining one or more components from an image may include normalizing and pre-processing the image to obtain a processed image. Features may be extracted from the processed image. Neural-network-based regression may then be performed on the set of extracted features to predict the one or more components. These techniques may be applied, for example, to the problem of extracting and removing bone components from radiographic images, which may be thoracic (lung) images.

Abstract: A technique for segmenting a ribcage, e.g., in a chest radiograph, may involve determining whether or not the top of the image cuts through the lungs. The process may then proceed to segment the top and the sides of the ribcage. A dynamic programming technique may be used to perform the segmentation.

Abstract: A technique for registering multiple image representations relating to a common region of interest may involve producing multiple displacements from the image representations for a key point. A final displacement for the key point may be determined from correlations of the multiple displacements.

Abstract: A method of obtaining one or more components from an image may include normalizing and pre-processing the image to obtain a processed image. Features may be extracted from the processed image. Neural-network-based regression may then be performed on the set of extracted features to predict the one or more components. These techniques may be applied, for example, to the problem of extracting and removing bone components from radiographic images, which may be thoracic (lung) images.

Abstract: An object in an image may be segmented by determining local pixel costs based on the image and using the local costs to determined cumulative pixel costs. A contour may then be determined based on the cumulative pixel costs.

Abstract: A method and system for enhancing the display of computer-aided detection marks on a medical image without obscuring underlying details are disclosed. A medical image is obtained and processed by a computer-aided detection (CAD) algorithm. The image is displayed or printed with its corresponding CAD-detected regions superimposed on the image. In one embodiment, the characteristics of the CAD-detected region will be enhanced in order to emphasis the suspicious image features. In another embodiment, a region surrounding the CAD-detection regions will be brighten with enhanced contrast by brightening the pixels associated with that region as compared to the rest of the image so that region surrounding the CAD-detected region clearly stands out. When viewed on a display, the user can toggle the CAD-detected region enhancements on or off. Further, the CAD-detected regions can be cropped from the medical image to further improve the visibility of image attributes.